An intelligent maintenance system (IMS) uses sensors, hardware processors, cloud applications and advanced analytics to improve the performance of maintenance for a machine, production line or manufacturing facility.

What’s So Smart About Intelligent Maintenance Systems?

Being a significant expense of any manufacturing company (commonly between 10% to 15% of total operating costs), maintenance has long been the focus of production efficiency efforts.

Industry 4.0 technologies such as digital twin and artificial intelligence are proving that even the best results from traditional maintenance methods can be improved upon by at least 30%.

That figure is a serious game changer for any manufacturer.

To achieve maintenance efficiency, a number of issues should be addressed:

  • In the case of a specific machine’s failure, what is its impact on the factory/plant’s throughput?
  • In the case of multiple machine failures, which maintenance job should be executed first?
  • What is the effect of the various failure types on production throughput and quality?
  • Which maintenance activities are possible to perform without affecting the production schedule?
  • What’s the most efficient use of factory/plant resources - labor, materials and equipment - for performing maintenance?

In this post, we'll look at how Intelligent Maintenance Systems help answer these questions, and in turn, improve maintenance efficiency.

 

So, what is an Intelligent Maintenance System?

“Intelligent maintenance system” is an umbrella term for a number of approaches that share a common goal: to improve the efficiency of maintenance activities through the use of digital technologies.

An intelligent maintenance system (IMS) uses sensors, hardware processors, cloud applications and advanced analytics to improve the performance of maintenance for a machine, production line or manufacturing facility.

 

How manufacturing facilities benefit from utilizing an IMS

The challenge in answering the questions above lies in the fact that they all depend on a large number of interlinked parameters; parameters that change over time, both in the short and long-term.

Fluctuations in environmental factors, the quality of raw materials, asset health status, workforce, market demand, and many others affect the required rate and type of maintenance.

So, how can manufacturers gain control over ever-changing operational environments?

The answer: by being able to predict.

One of Industry 4.0’s most powerful use cases is predictive maintenance which leverages data captured from sensors, PLCs, data historians, ERPs, MESs etc. to form failure predictions.

Machine learning and other AI algorithms such as artificial neural networks are used to process this data constantly. The algorithms search for correlations that can help determine the root cause of recurring problems that lead to unplanned downtime.

artifical neural network schematic
Schematic of an artificial neural network. ANNs are used to discover causal correlations between root causes and failures.

The ROI of IMS

An IMS with predictive maintenance capabilities will autonomously alert relevant personnel about issues and only recommend maintenance activities when necessary.

In this way, maintenance efficiency is significantly improved, offering manufacturers a number of benefits that positively affect the bottom line:

Lower maintenance costs - Repairs are performed when needed instead of according to a predetermined schedule (which in many cases leads to redundant checks/maintenance activities).

Increased uptime - With IMS, downtime can be scheduled, and as a result is usually much shorter than what’s needed for reactive repairs.

Reduced labor costs - Maintenance teams are smaller since tasks are planned beforehand.

Lower equipment costs - Maintenance focuses on the problematic components only. This prevents wear and tear to surrounding parts during the repair and prevents unnecessary replacements.

Lower inventory expenses - Since problems are predicted, orders are made only for materials and components that will be needed in the near future.

Lower chance of secondary damage - Because problems are detected early on, they can be dealt with before more extensive damage is done to equipment.

Increase in Remaining Useful Life (RUL) - The root cause of issues is pinpointed making the need for disassembly less frequent.

Quality 4.0 - By improving asset health, deviations in performance become far less frequent, leading to consistently high levels of output quality.

 

Maintenance doesn’t have to be a sore point

By deploying an Intelligent Maintenance System, today’s manufacturers have the opportunity to overcome maintenance challenges and gain control over complex production issues.

 

Contact us to find out how your operation can reduce unplanned downtime and prevent quality deviations using intelligent maintenance.

For more information about everything Industry 4.0, head on over to our free resource library.

 


In manufacturing, digital twin solutions represent “real-world” equipment, machines, factories, and plants.

5 Ways Digital Twin Solutions
Predict and Resolve Manufacturing Issues

A digital twin is a digital replica of a physical asset, device, system, place, or person.

In manufacturing, digital twins are used to represent “real-world” equipment, machines, factories, and plants.

Sensors placed upon machinery across an entire production line, for example, feed data through an IIoT network to be accessed via a dashboard. The data is displayed as a visual model - the twin - and acts as a live representation of that line.

This offers the ability to drill down into specific assets for improved root cause analysis and a host of new use cases for improved manufacturing operations.

Digital twins are helping companies surpass previous performance levels in various aspects of manufacturing, from the product design phase to supply chain management.

Here's how:

1. Monitoring

A digital twin merges live data from its physical counterpart with an interactive visual interface. This offers an unsurpassed level of monitoring.

Asset and production line performance data is presented in the context of the physical device, usually with a 3D visualization of the part/machine/line.

This enhanced visual interaction improves the ability to formulate actionable insights based upon the presented data.

By combining next-level monitoring capabilities offered by digital twin with machine learning algorithms, manufacturers are able to perform automated root cause analysis to prevent recurring asset failures and quality deviations.

The Seebo Predictive Maintenance Software Platform
The Seebo digital twin dashboard. Predictive maintenance and quality alerts are provided in the context of the production process.

2. Maintenance

The improved monitoring capabilities mentioned above feed directly into maintenance efficiency.

Operators and technicians are provided with detailed information about the health of every asset. This leads to insight that can be acted upon directly to improve OEE.

A digital twin isn’t only a graphical model. Predictive analytics powered by machine learning and other AI algorithms dissect the data, search for correlations, and formulate predictions about remaining useful life (RUL).

Digital twin solutions take maintenance from a reactive to a predictive approach. Repairs are only performed when needed and components aren’t switched out unnecessarily.

Since maintenance is planned and only done when necessary, technical teams need not be so large and arrive on-site with the right tools and parts, as well as precise instructions for the repair procedure.

3. Training

Digital twin is an excellent tool for professional training due to its visual interface and the fact that it mirrors real-life scenarios from the production floor.

Digital twin can be used for broad-topic training such as site orientation and safety protocols or specific technical training such as repair and installation procedures.

4. Communication

A digital twin can play a vital part in helping employees share knowledge about production issues.

Automatic alerts about predicted failures or quality deviations can be viewed by all the relevant personnel. Tips and advice can be shared, including highly specific technical details thanks to the visual nature of the twin.

This information is archived and accessible to personnel in other facilities, streamlining root cause analysis and preventing the unnecessary repetition of mistakes.

5. Strategy

A digital twin solution can be extremely useful in testing new concepts for optimization without needing to disrupt production.

Major changes can be made to the operation without going into downtime.

A digital twin can provide insight across all stages of the product life cycle by:

  • Refining assumptions through the use of predictive analytics.
  • Establishing a digital thread that connects individual systems to improve traceability.
  • Visualizing the use of products in the field, in real time.

 

How to find the right digital twin solution for your operation

Manufacturers looking to deploy a digital twin should evaluate candidate solutions against the following key capabilities:

Simple visual interface - The digital twin platform should be clear and intuitive, easy to navigate, and simple to interact with for personnel of all backgrounds: process engineers, maintenance and quality teams, plant managers etc.

Detailed and process-based - The twin should be capable of displaying a wide range of data from individual components to production lines consisting of multiple machines as well as data on HVAC, inventory, and supply chain systems.

Big Data Processing - The twin, along with the industrial IoT network set up in the facility, must be able to handle large volumes of data simultaneously for real-time processing.

Analytical - The digital twin solution should offer a variety of AI algorithms that are easy to apply and that output actionable insights that are relevant to the needs of your operation.

Customizable - Every manufacturing facility is different. Dashboards, alerts, and other system settings should be configurable to suit the nature of the work at your plant or factory.

Download our free digital twin whitepaper, or get in touch for a 1-on-1 demo to see how a digital twin can significantly improve your operation’s uptime and output quality.


Tips for Choosing Predictive Maintenance Software

Looking for Predictive Maintenance Software? First, read this...

Predictive maintenance (PdM) is probably the business use case most responsible for drawing manufacturers to Industry 4.0 adoption.

Preventive maintenance for production line assets (common practice until the advent of Industry 4.0) is a major cost burden for manufacturers. In fact, 40% of all preventive maintenance costs are spent on assets with a negligible effect on actually preventing failures.

It's no surprise that the ability of predictive maintenance to predict unplanned downtime events and quality deviations is proving to be a game changer.

This is due to the 8 main benefits of predictive maintenance:

  1. Reduction in lost production time - PdM allows for planned downtime which is usually much shorter than what's needed for reactive repairs, and can be scheduled for times that are convenient and less costly.
  2. Reduced maintenance costs - Repairs are done when needed, instead of routine maintenance which in many cases is redundant.
  3. Lower labor costs - Technicians are called upon for specific and focused tasks.
  4. Reduced equipment costs - Only the problematic components are dealt with, preventing unnecessary replacements and the wear and tear of adjacent parts caused by repair.
  5. Lower chances of secondary damage - PdM identifies problems early on before they escalate and cause more extensive damage to equipment.
  6. Reduced inventory expenses - With PdM, orders can be made only for the parts and materials that are needed.
  7. Longer lasting machinery - Since disassembly is carried out less frequently, equipment lasts longer, increasing remaining useful life (RUL).
  8. Reduced risk-based costs - Fewer unplanned repairs reduce safety risks and the chance of damage being done to other parts or equipment.

 

Preventive Vs. Predictive Maintenance
Preventive Vs. Predictive Maintenance. While preventive maintenance has been common practice for decades, predictive maintenance requires less labour and is significantly better at preventing failures.

What is predictive maintenance software?

Predictive maintenance is a method of preventing machine failure by analyzing machine data to identify patterns and predict issues before they happen.

At the intersection of human-machine interaction within the smart factory, predictive maintenance software gives manufacturers monitoring and control over PdM capabilities.

For this reason, it’s critical that PdM software offer users visual, real-time interaction that’s accurate,  reliable, and can be customized.

What to know about your company’s needs

Every manufacturing facility operates differently. Clearly understanding your operation’s needs, priorities, and economic dynamics is key in implementing the predictive maintenance software that will deliver the highest ROI for your operations.

The opinions and experience of engineers, technicians and management personnel should be included when deciding upon which software to deploy.

Answer these 8 questions to get a better idea of your company’s needs:

  1. What is the monthly cost of unplanned downtime to the business?
  2. What are the common root causes of production disruptions?
  3. What are the common root causes of quality deviations?
  4. What type of problems are you looking to predict - mechanical failures (e.g. motor or bearing failures) or process-centric issues (e.g. deviations from recipes)?
  5. What is the level of in-house data analytics expertise?
  6. Who will be using the software?
  7. Is data from the production lines (OT) accessible to external systems? Is it persisted in a database (e.g. data historian)?
  8. Is data from operational systems integrated with data from business systems (IT) and process flows to enable effective and accurate data analytics?

What to look for in a predictive maintenance software solution

Here are 6 key capabilities to consider when evaluating predictive maintenance software solutions:

Industrial artificial intelligence - AI algorithms integrated into the platform that can be used to drive efficiency through use cases that are relevant to the specific manufacturing type. For example, mechanical failures and process-related issues each require specific types of machine learning.

Simple and intuitive - Predictive maintenance software should be easy-to-use for operators, technicians and management. For example, a digital twin interface can display PdM insights and supporting data in the context of the production line, allowing for quick and intuitive root cause analysis.

Human in the loop - Predictive maintenance software should be able to receive input from production line experts in parallel to data from sensors. In this way, human experience can be leveraged for more accurate predictions as the algorithm learns from expert knowledge.

Actionable and prescriptive - Insights from the software should lead to action, with information on exactly what needs to be done, and how. By pinpointing a predicted failure, technicians perform the prescribed set of corrective actions by accessing and checking off standard operating procedure tasks.

Measurable outcomes - Predictive maintenance software should be able to report on its value and the improved business outcomes using quantifiable metrics.

Compatibility - It’s common for production lines to consist of industrial assets manufactured by a variety of OEMs. PdM software should have the ability to work with different types and brands of assets, seamlessly connecting data historians and PLCs while integrating IT systems (ERP, MES, QMS, etc.)

 

A condition monitoring dashboard of the Seebo platform showing predictive alerts.
The Seebo platform. Predictive maintenance and quality alerts are provided in the context of the production process.

Always check what’s in the box

The predictive maintenance software market is diverse both with regards to the solutions themselves, and the support and accompanied services offered by software vendors.

Make sure you know exactly what the PdM software does - what’s included, and what’s not.
Many software solutions require significant customization to be performed by the vendor’s professional services. Make sure to factor in the extra costs of additional service providers or software applications.

Planning is everything

Once all of the above has been taken into account, there should be one underlying principle guiding you to the right choice - quick and easy deployment.

Consider solutions that can be deployed to deliver value in under 3 months, and that can be continually and easily adapted as the business requires.

Leading vendors will be able to offer you a customized deployment timeline. The timeline should include all the various deployment stages to help you configure the PdM system through prototyping, validation and finally, solution delivery.

For more in-depth information about predictive maintenance, download our free whitepaper.

Get in touch to see how PdM can significantly cut your maintenance costs and improve the performance of your production line.


Asset Performance Management in Food & Beverages Manufacturing

Asset Performance Management in Food & Beverage Manufacturing

The cost of maintenance is a common pain point amongst food and beverage manufacturers, and for manufacturers in the process industries at large.

Partial digitization has enabled manufacturers to marginally improve operations, but many factories and plants are still battling with costly maintenance and unsatisfactory OEE levels, often below 65%.

With the advent of Industry 4.0, food and beverage manufacturers now have the opportunity to make continuous improvements to maintenance activities that affect the bottom line.

The missing part of the puzzle:

Strategy.

The elements of an industrial IoT system - sensors, gateway devices, edge and cloud computing, connectivity protocols - are all implemented to gather and organize data.

By applying machine learning algorithms to this data, manufacturers can gain new and tangible operational efficiencies, but this requires planning and the allocation of resources.

Enter Asset Performance Management.

What is Asset Performance Management?

The term “Asset Performance Management” (APM) refers to a strategic approach that employs dedicated software applications and tools to improve the availability and performance of physical manufacturing assets.

Assets may include equipment, production machines, and factory infrastructure systems.

APM has a number of benefits:

  • Reduction in unplanned downtime
  • Increase in the availability of critical assets
  • Cuts in maintenance costs
  • Improvements in maintenance efficiency
  • Reduced environmental, health and safety (EH&S) risks

APM and Food & Beverages - A Perfect Match?

The last item in the list of benefits above is especially important in the food and beverage manufacturing sector. APM can help companies operate in compliance with ever-changing government EH&S regulations.

When machines run efficiently, waste is reduced, and safety conditions are improved as a natural byproduct.

With the predictive analytics capabilities of Industry 4.0 technologies, operators, technicians and managers are notified of imminent component, machine, or process failures well in advance.

This allows for proactive maintenance - repairs can be planned, resulting in better use of human resources and equipment. Inventory is better managed avoiding unnecessary and expensive emergency purchasing of spare parts.

Most importantly, if downtime is unavoidable, it’s scheduled so that disruptions are minimal.

APM in Food Manufacturing

Many factories and plants have avoided tackling maintenance efficiency head on. This is partly due to a somewhat conservative mindset that “accidents happen” and that preventive maintenance is good enough.

The truth is that reactive and scheduled maintenance is extremely costly, especially when it results in unplanned downtime. According to a T.A. Cook report on maintenance efficiency, 45% of maintenance activity is of no value at all.

By introducing Asset Performance Management as a consolidated strategy, food and beverage companies have the chance to leverage IIoT technologies to stay competitive and improve quality.

 


Predictive Maintenance Tools & Use Cases

Predictive Maintenance Tools & Use Cases - Part II

With the core elements (covered in Part I of this article series) in place, the predictive maintenance system can be deployed with the industrial IoT platform acting as a Human-Machine Interface (HMI) for continual monitoring and control over the assets...

 

Industrial IoT Platform (Deployment Phase)

Function: Deployment and continued interaction with the predictive maintenance solution

The IoT Platform acts as the predictive maintenance hub, notifying management and relevant personnel about any issues that require attention in real time.

Alerts are sent automatically with relevant repair information allowing for teams to collaborate and form a plan of action long before the failure is predicted to take place.

A condition monitoring dashboard of the Seebo platform showing predictive alerts.
A screenshot of the Seebo platform - a condition monitoring dashboard shows sensor information and predictive alerts.

Data Analytics

Function: Pattern detection for actionable insights that improve OEE and output quality

Through the deep analysis of historical and real-time machine data, management can be given accurate insight into the operation’s performance. Unplanned downtime nears zero since personnel is notified ahead of time of any impending issues and is given detailed instructions on the type of repair needed to prevent malfunction.

AI algorithms provide an unbiased evaluation of all aspects of the production process and can perform advanced root cause analysis to reveal dependencies that can be difficult for even very experienced professionals to detect.

The fact that the analysis is continual means that informed decisions can be made in real-time to cut the loss of defective output caused by quality issues.

 

Workflows

Function: Optimize data flow throughout the network, allowing for problem-free scaling

Workflows are a strategic predictive maintenance tool that network managers use to help the manufacturing facility make optimal use of the system.

Workflows offer two main functions:

Device management - device registration, protocol interoperability, authentication, and access.
Event processing - the polling of data events, and the routing of data to the required destinations eg. feeding existing ERP and CRM systems.

 

PdM Pilots

Function: Provide a testing phase to ensure stakeholder buy-in and significant ROI.

For the successful deployment of a predictive maintenance system, it’s important to first launch well-structured and monitored pilot programs to test the waters before full adoption.

This is an opportunity to gauge the ROI of predictive maintenance for the specific operation, and to give stakeholders the chance to weigh in to the project.

While the pilot is being run, modifications can be made via the industrial IoT platform until the predictive maintenance system reaches a satisfactory level of performance.

 

Learn more about predictive maintenance tools and use cases by reading our complete guide to predictive maintenance or download our extensive white paper for free here.

 


Predictive Maintenance Tools & Use Cases

Predictive Maintenance Tools & Use Cases - Part I

Predictive maintenance is used to prevent unplanned downtime by leveraging advanced data capturing and analysis techniques.

The many benefits that arise from being able to predict failures and quality issues make predictive maintenance invaluable to any manufacturing operation.

Until now, operators and maintenance personnel have performed maintenance according to a preset schedule, also known as “preventive maintenance”. This method can consume unnecessary labor and material resources, and results in maintenance activity that only prevents about 50% of a factory’s failure events.

Join us in this 2-part article series covering predictive maintenance tools and use cases including insight into PdM in both setup and deployment phases.

 

How does Predictive Maintenance work?

Part of the Industry 4.0 revolution, predictive maintenance makes use of a set of tools such as process modeling, data integration, data analytics and machine learning - to provide manufacturers with actionable insights about the health of their production assets.

For a general idea of how this is achieved, this post will include a list of predictive maintenance tools including the components of a typical predictive maintenance system.

 

Predictive Maintenance Tools - Setup

A predictive maintenance system is set up using a number of tools, each with its own defined functionality:

Sensors

Function: Capture data in real time

The market for industrial sensors is constantly growing in variety and quality with a vast number of vendors offering sophisticated products at relatively low costs.

Some common parameters captured by industrial sensors include:

  • Vibration
  • Temperature
  • Pressure
  • Light
  • Water/lubricant quality
  • Chemical content
  • Liquid/solid levels

In manufacturing, data sent from these sensors can be used for predictive maintenance. Through the application of advanced analysis techniques such as machine learning and artificial neural networks, predictions are formed regarding Remaining Useful Life (RUL) and other asset health and performance-related KPIs.

The data captured by sensors can also be utilized in other manufacturing use cases such as:

Track & Trace - following raw materials, components, mobile equipment, and finished products throughout the factory/process.

Quality Assurance - testing using optical sensors and identifying equipment anomalies that will likely result in quality issues.

Inventory Management - monitoring and controlling the supply of spare parts and raw materials, preventing inventory surplus or shortages.

 

Data Communication and Gateway Devices

Function: Facilitate communication in the Industrial IoT network

Gateway Devices are intermediate connection points that connect controllers (PLCs, wireless devices etc.), and sensors to a computing platform - whether on-premise or on-cloud. Gateway devices also protect the IoT network and the data being transported, providing an additional layer of security.

IoT Communication Protocols form the language of the Industrial IoT network. Common protocols such as MQTT, AMQP and CoAP, make sure that devices across the network speak the same language (interoperability) while being light on resources such as power and memory consumption.

 

The Edge & Cloud Nodes

Function: Data aggregation and device management

Edge computing in the smart factory setting.
Edge nodes maintain the security of the industrial IoT network while reducing latency in communication.

Even a fairly simple production line can generate massive amounts of data which need to be captured, aggregated, normalized, and analyzed.

To secure the network, and to maintain a low level of data communication latency, edge nodes are deployed in close proximity to the production line. The edge nodes handle part of the analytical and processing workload. This also makes scaling the network much easier.

 

Industrial IoT Platform

Function: Modeling, simulation, data integration, and predictive analytics

Building in-house solutions for predictive maintenance is expensive and heavy on resources, while many companies lack the relevant skills.

Industrial IoT platforms vary in the scope and quality of their features, but the more comprehensive platforms will usually offer the following capabilities:

  1. Solution Modeling - Creating a visual blueprint of the PdM solution that includes the various components within the context of your operation: physical equipment, sensors, communication protocols, data analytics, and dashboards.
  2. Simulator - A test environment to validate the functionality and cost of your PdM system before development.
  3. Data Integration - Gateways and connectors enabling streamlined IIoT development with connectivity to your OT and IT data sources.
  4. Predictive Analytics and Machine Learning - Insights from the platform are used by operational teams on the ground to uncover the root cause of manufacturing issues, maximize overall equipment effectiveness (OEE), and reduce unplanned downtime.

BONUS: Digital Twin - Leading IIoT platforms provide intuitive visualization of the condition of production lines in real time. Alerts and automated root cause analysis, powered by machine learning and AI, deliver deep insight into upcoming downtime and quality disruptions.

 

Stay tuned for Part II!
Learn how predictive maintenance is deployed in the manufacturing setting, and how an industrial IoT platform is used as a Human-Machine Interface for maintaining control over asset health.

 


Overall Line Efficiency - An Important Metric for Industry 4.0

Why Overall Line Efficiency is a Necessary Metric for Industry 4.0

Overall Equipment Efficiency (OEE) is a widely accepted and utilized manufacturing evaluation method, but Industry 4.0 is raising the standards of production, and OEE is limited in its ability to take into account more complex systems.

To respond to this need for a better-suited metric, a technique known as Overall Line Efficiency (OLE) is being used, mostly because of its ability to describe multiple production lines and the interaction of a number of various sub-processes within a larger production process.

A complete performance evaluation approach will incorporate both OEE and OLE methods with appropriate modifications to suit the operation.

 

Efficiency Vs. Effectiveness

To differentiate between OEE and OLE, it helps to start by clarifying the difference between effectiveness (in OEE) and efficiency (in OLE), two terms often misused in the context of manufacturing.

 

Differentiate between OEE and OLE

 

The simple diagram above demonstrates that effectiveness focuses on performing the right tasks and aiming for the right goals while efficiency is about performing tasks in an optimal way.


Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness is a fundamental KPI that is used to improve manufacturing processes by using benchmarking and analysis to pinpoint inefficiencies and to categorize them.

OEE seeks to describe the overall utilization of materials, equipment, and time in a production process. OEE is calculated according to the below equation, although there are a number of ways of defining the 3 contributing parameters...

 

OEE  = Availability X Performance X Quality

For production lines consisting of a number of unbalanced (unpaced) machines, OEE is not ideal, being better suited to evaluate individual assets.

 

Overall Line Efficiency (OLE)

Overall Line Efficiency is a fairly new metric in manufacturing and builds off of OEE to compare the current performance of a production line with how well it could be performing.

OLE also takes into account the personnel involved in the various processes, seeking to optimize the synchronization between the output rates of machines and the use of human resources.

 

OLE   =   OEE of Machine A + OEE of Machine B + OEE of Machine C
3


The above calculation assumes that the importance of Machine A, B, and C are the same ie. have the same “weight”. In most manufacturing scenarios this will not be the case, with different processing stages having different weights, resulting in a more complex OLE calculation.

Overall Line Efficiency can be expanded further to include a calculation for each production line (taking into account the bottleneck for each), and to formulate a calculation that incorporates a number of production lines.

 

New Methods for Calculating OEE and OLE

The use of artificial intelligence is steadily growing within the manufacturing sector and can be applied to both OEE and OLE calculation. AI’s advantage here is its ability to adapt to different manufacturing scenarios thanks to the algorithms’ flexibility.

In other words, an AI algorithm used to calculate OLE isn’t affected by whether the operation is in the aerospace or food processing sector and the meaningful differences between those sectors can be reflected in the algorithm by setting specific weight values for critical parameters.

 

Using Artificial Neural Networks for Overall Line Efficiency

Artificial Neural Networks can easily handle the complexity of OLE calculation, and lead to far more accurate results than those achievable through more traditional calculation methods.

Implementing ANNs to calculate Overall Line Efficiency is not an immediate process - the algorithm needs to be trained. This is done by feeding the ANN existing historical data categorized as input (OEE) or output (OLE) along with other relevant data from the machines and production floor. ANNs can also be fed data from observations made by operators, enhancing the training through additional information layers.

 

Better Manufacturing Management with Overall Line Efficiency

Using a combination of OEE and OLE calculations to monitor the performance of a manufacturing operation can be extremely useful for management. Due to the high number of variables involved, artificial intelligence in the form of Artificial Neural Networks and other techniques, is very well suited to this field and can offer actionable insights for better management decisions and greater impact.

 

 


How to Choose the Right Condition Monitoring Solution.

How to Choose the Right Condition Monitoring Solution for your Operation

Condition Monitoring plays a major part in the smart factory ecosystem, providing a foundation for Predictive Maintenance, Predictive Quality, and other Industry 4.0 applications including Digital Twin.

However, data acquisition, data communication, machine learning, visualization, security and other aspects of industrial IoT implementation can prove to be too challenging and risky for manufacturers lacking the required skill sets internally.

For this reason, companies are increasingly turning to external turnkey Condition Monitoring solutions, but as the market for this type of service grows, it’s becoming difficult to make sense of what each Condition Monitoring vendor offers, and how they compare.

Below is a breakdown of the most important factors to consider when evaluating a Condition Monitoring solution.

For a full guide with everything you need to know about choosing a Condition Monitoring solution, make sure to download our free whitepaper:
How to Choose the Right Condition Monitoring Solution.

 

Capabilities every Condition Monitoring solution should offer...

Solution Modeling

The goal of creating a solution model is to visualize all the use cases involving the connected assets, across all processes, and in as much detail as possible, including machines, sensors, gateway devices, PLCs, connectivity protocols, analysis, and dashboards.

A Condition Monitoring solution should offer the ability to create a visual prototype of the proposed system, including a connectivity blueprint and a list of necessary sensors and other hardware/software.

Simulation

Simulating use of the proposed system with relevant Condition Monitoring use cases minimizes risk and provides a great opportunity for stakeholder buy-in.

Engineers, technicians and management can bring to the surface issues regarding the system, and provide feedback that can be implemented to make further improvements.

Data Acquisition

In any Factory 4.0 use case, data is of key importance. Acquiring data needs to be a precise, organized, and continuous operation to ensure that the system receives an accurate real-time representation of what’s happening on the factory floor.

To get the full picture of an asset’s health, or that of a system/production line, it’s important that a Condition Monitoring solution has the ability to “connect” with your existing data system: ERP and historian data sources, as well as PLCs and sensors.

Visualization

Dashboards play an important role since they present the data in a way that can be interpreted to inform management decisions about maintenance tasks and other aspects of production.

Every manufacturing process is different, meaning that dashboards should be customizable, suiting the metrics and KPIs that are most important to the manufacturer, and offering the ability to set up rules for critical alerts.

Digital Twin Analytics

A Digital Twin represents the attributes of a factory, plant, or asset in real time. This data-rich interface improves anomaly detection and the identification of weak links in processes making it a very capable tool for Condition Monitoring.

Agile & Collaborative

A Condition Monitoring solution should include access to a platform that can generate a digital prototype. This prototype is crucial for keeping the development process agile and collaborative, allowing a wide scope of professionals - technicians, engineers, managers - the ability to review the proposed system. Most importantly, such a platform is likely to prevent errors in the planning of a Condition Monitoring system; errors that can be expensive to remedy further down the line.

The platform should allow for modifications to be made even after the system is in production so that improvements can easily be made, and new business demands met.

Need help with Condition Monitoring?

Seebo offers a complete Condition Monitoring solution for OEMs and plants/factories that are looking to leverage Industry 4.0 to impact their bottom line.

Book a live demo and take the first step in your digital transformation by exploring how Seebo can help you rapidly generate a Digital Twin prototype for your enterprise or asset.

 

 


Condition-Based Monitoring is Changing the Food Processing Industry

How Condition-Based Monitoring is Changing the Food Processing Industry

The food manufacturing industry is often slow to adopt new technologies because of health and hygiene regulations, but the advantages of Industry 4.0 are proving to be too significant to be ignored.

Drops in the cost of hardware (sensors, gateway devices, connectivity solutions, and cloud computing) along with improved software tools, have created a compelling case for implementing Industrial IoT (IIoT). And with proven business benefits of smart factory technology already delivered in the food and beverage industry - through Condition Monitoring and Digital Twinning - there is mounting pressure on food manufacturers to keep up with the competition.  

 

The Unique Challenges of Food Processing

Since the end-product is always intended for human consumption, food manufacturers face numerous unavoidable and significant costs including pest control, microbial testing, hygiene consultation and other services.

Ongoing compliance with food and beverage safety regulations demands that sanitation equipment be constantly modified. The importance of cleanliness results in production zones becoming wet environments with moisture levels high enough to damage equipment.

Food manufacturing processes are extremely elaborate and incorporate numerous stages from mincing, liquefaction and emulsification to cooking, pasteurization, and packaging. These processes demand machinery that is highly complex, making efficient maintenance a real challenge.

Condition Based Monitoring Dashboards - Food Processing Industry

Condition-Based Maintenance in Food Processing

To combat the complexity and cost of asset maintenance in food production, Condition Monitoring is used to regularly deliver a large part of the necessary data to perform Condition-Based Maintenance.

The data is captured by sensors placed on machinery that constantly capture a variety of data sets that can be used to monitor the health of an asset.

Common Condition Monitoring techniques in the food industry include vibration analysis, oil analysis, and thermal imaging.   

 

Predictive Maintenance in the Food Production Industry

The data collected through Condition Monitoring, combined with historic and ERP data about the various machines, can be aggregated, forming the basis for Predictive Maintenance.

Detecting deviations and performing analysis on the data using Machine Learning results in the ability to formulate predictions regarding equipment failure. In this way, preventive maintenance schedules become redundant since maintenance is only carried out when necessary, cutting significant costs in labor and minimizing unplanned downtime.

 

5 Major Benefits of Industrial IoT in Food Production

Food Safety

One of the most important parameters to monitor in food safety is temperature, but that’s not always a simple task considering the variety of processes and environments food is exposed to before it reaches its final point-of-sale.

The latest sensors designed for IIoT use are accurate, reliable and inexpensive, making temperature tracking extremely simple, regardless of which step of the process the product is in.

Track & Trace

IIoT enables real-time inventory tracking and detailed monitoring of the arrival and processing of raw materials, transport activities within the plant, and product distribution.

Plant conditions can be continually monitored to ensure employee safety and that of prepared food items and raw materials.

Remote Monitoring

A smart factory can be monitored remotely and grants management the ability to view an operation from a micro-to-macro viewpoint.

Beyond the evaluation of a single machine, process or facility, Remote Monitoring offers the ability to log, track and compare various processes across various facilities, and can provide valuable insights on how to optimize these processes.

Access to Data Insights

The wealth of data collected from Condition-Based Monitoring is useful because of how it can be leveraged to cut maintenance costs, but another significant advantage is that the information can be accessed by authorized personnel in a range of professions.

This makes transferring tasks much more efficient since the data on any asset is available anytime, anywhere. Also, because this detailed information is shared with employees with a variety of skill sets, it can be leveraged across the company to make changes that will directly impact the bottom line from different angles.

OEE

Performing maintenance using a data-driven approach offers a host of advantages for the food processing industry. Machinery can be closely tracked for Overall Equipment Efficiency (OEE) preventing wastage and food safety issues while unplanned downtime is dramatically reduced since maintenance tasks are only performed when necessary.

 

Taking Food Processing to the Next Level with IIoT

Industrial IoT is already having a significant impact on the food processing industry, with a growing number of companies implementing the solutions mentioned above.

And the future is bright - there’s still a lot more unchartered territory with regards to how this technology can be used in the food processing sector to further reduce downtime, increase throughput, and improve product quality.

Looking for a Condition Monitoring solution, but don’t know where to start?

Download our free whitepaper and learn how to find the best Condition Monitoring solution for your operation.

 


Why IoT Prototyping is Crucial to IoT Success

IoT introduces new complexities to product development: the introduction of data connectivity, data acquisition, data management and security, data analytics, cloud services, and dashboards.

As complexity rises, so does the cost of fixing errors, and because of the physical aspect of IoT systems - sensors, gateway devices, PLCs, routing - building a system that doesn’t meet the project’s needs can be very costly to remedy.

IoT prototyping - iot development lifecycle and costs

Correctly planning your IoT project by using the right tools and setting up a detailed work strategy upfront can end up saving you months in development, and a significant amount in costs.

General Tips for IoT Prototyping

Define your target

If you don’t know where you’re heading, how will you know when you’ve reached your destination? Clearly defining your prototype objectives and critical success factors is imperative to making sure the project will benefit from a positive ROI.

Choose your IoT platform carefully

The IIoT platform you use will play an integral part in the success of the prototype, the development process, and the resulting on-site system. There are many types of platforms available so be sure to do your due diligence when choosing an IoT platform since making the move from prototype to actual system will be much smoother without having to switch platforms.

Work with partners

Prototyping will always be a time-sensitive process which is why it’s important to have a deadline and a clearly defined roadmap. Implementing IoT into a manufacturing operation is complex. Be aware of what your strengths are, and those of your team, and know when to seek out professional IoT services. This can cut costs in the long run and save a considerable amount of time.

Don’t Cut Corners - Thorough Prototyping is Crucial for IoT Success

The IoT implementation process requires skillful orchestration. There’s no reason not to aim for the most significant ROI on your IoT project, but as with all initiatives, planning, using the right tools, and collaborating with the right partners is crucial.