With clear benefits and positive ROI already reported by leading manufacturers, Predictive Maintenance powered by Machine Learning is proving to be a driving force in the new wave of manufacturing excellence.

Manufacturers are constantly under pressure to stay competitive by improving efficiency, reducing maintenance costs, optimizing processes and using analytics to make better business decisions. With AI, specifically Machine Learning in industrial applications, manufacturing is taking another step towards ultra-automation.

There’s only so much a human can do when faced with extremely large amounts of data that are constantly growing. And while there’s no lack of software and methodology for human-sourced data analysis, Machine Learning presents a whole new approach to the constant need of manufacturers to improve efficiency, safety, and quality of product.

Machines Learning about Machines

Machine Learning (ML) is a method of computerized self-learning that resides at the centre of most AI applications. ML models combine advanced pattern-learning abilities with the ability to self-educate and adapt as changes occur within the input data.

In Machine Learning, algorithms and systems don’t rely on explicit programming, but instead improve their own performance by collecting data based upon experience. Since this experience can include literally trillions of observations, Machine Learning algorithms have the power to form predictions, and continually learn and improve accuracy by recording “hits and misses”.

This predictive ability is what lends Machine Learning to Predictive Maintenance, leveraging new and existing sophisticated prediction algorithms to optimize maintenance, improve quality and boost production throughput.

Maintenance: From Preventive to Predictive and Beyond

Maintenance is one of the core activities of any manufacturing enterprise.

It also represents a major part of any facility’s expenses which is why Predictive Maintenance has become a common goal amongst manufacturers looking to leverage its many benefits, including the reduction of unnecessary scheduled repairs ie. Preventive Maintenance.    

Until now, manufacturers that do perform Predictive Maintenance have done so using SCADA systems. This means that the thresholds, rules, and configurations that decide how the system reacts to the situation on the shop floor are programmed and analyzed by humans.

These human-developed rules and coarse threshold configurations are constraining to a certain extent since they don’t take into account dynamic behavioral patterns. Nor do they take into account the contextual data of the manufacturing process.

Take for example, a sudden sharp rise in temperature in a specific sensor within a specific machine in the production line. A rule-based system – that is not privy to the context of the machine being sterilized at that specific point in time – would alert the operator of a potential problem.

ML algorithms are fed OT data (production floor data), IT data (production context data – from ERP, Quality, and other IT systems), and manufacturing process data (which describes the interdependencies between the machines and the production flow).

These ML algorithms are trained to detect anomalies and test correlations in search of patterns in the data. Machine Learning can analyze a vast amount of data in real time, evaluate the behavior of an asset or system, and identify the deterioration of a machine or component before a malfunction takes place.

Human-in-the-Loop: Adding Speed and Accuracy

After stating how Artificial Intelligence surpasses humans in advanced analysis, it’s important to note the importance of keeping the human element within the Predictive Maintenance learning system. The Human-in-the-Loop (HITL) approach is a means of leveraging the best of both worlds.

The idea behind HITL is that human feedback to the relevance of AI output can accelerate the Machine Learning process towards a high accuracy rate. In HITL, data scientists iteratively train, tune, and test the machine learning algorithms. Human-in-the-loop also includes making the right decisions based upon ML predictions.  

Machine Learning for Better Asset Maintenance

Predictive Maintenance tools makes use of Machine Learning algorithms through two main approaches.

Both these approaches have the same goal: to identify specific relationships or characteristics in the input data (from the manufacturing process) that produce target results in the output data, efficiently.


As the name suggests, Classification is ideally used with data that can be categorized. A classic example that we’re all familiar with is the email filter algorithm that decides whether an email message is classified as spam or not.

With this technique, the output is limited to a boolean value, but the upside is that a high level of accuracy can be achieved with a relatively small amount of data. In Predictive Maintenance, Classification predicts what the probability of a failure is within the next n steps (with n defining the class division ie. by time units, cycles etc.)

Commonly used classification algorithms for Machine Learning include naive Bayes, logistic regression, random decision forests, and Artificial Neural Networks.  


Regression is used when data exists within a range. This is often the case with data captured via sensors, and Regression can be used to predict or estimate a response for one or more continuous values.

Using Regression, we can formulate a prediction of how much time remains before the next component/machine/process failure, or in other words, Remaining Useful Life (RUL). Linear Regression is the most common regression algorithm since it’s quick to implement and the output is easy to interpret.

Artificial Neural Networks

One of the main algorithms used in Machine Learning is the Artificial Neural Network. As the name implies, these systems are inspired by the way the human brain learns and processes new and stored information using its network of around 100 billion neurons.

In manufacturing, Artificial Neural Networks are excellent for simulating production processes, demonstrating both efficiency and accuracy, and the ability to optimize multi-response parameters. 

predictive maintenance and machine learning - artifical neural network schematic
A basic schematic of a feed-forward Artificial Neural Network. Every node in one layer is connected to every node in the next. Hidden layers can be added as required, depending on the complexity of the problem.

Implementing Artificial Neural Networks is greatly beneficial to manufacturing processes since they can create predictions about the mechanical properties of processed products, enabling cuts in the cost of materials.

Machine Learning Use Cases in Manufacturing

There’s already a large number of use cases for machine learning in  process manufacturing, discrete manufacturing, energy production, logistics, and transportation.

Process Manufacturing

Process manufacturing processes – such as in the food, chemicals, and plastics industries – can be extremely complex, often including dozens of distinct manufacturing stages. For this reason, focusing ML efforts on a single machine or stage will not yield optimal results.

Instead, the algorithms can be fed data from multiple machines and processes, enabling the discovery of subtle and often-unexpected correlations.

In process manufacturing, one of the focus points for improvements is quality, which has a direct effect on an operation’s bottom line. Using Machine Learning, it’s possible to predict product quality issues, provide the root-causes to prevent them from occurring, and reduce wastage.

Customer-focused Manufacturing

The essence of manufacturing is changing. Customers have come to expect more from products, and more options from manufacturers.

Machine Learning will play a large part in allowing manufacturers to create products that are inline with the ever-changing demands of customers. For this to happen, manufacturers will have to be able to receive usage data from products in the field. With this data as input, manufacturers will be able to understand customer habits and needs, creating highly customized products, improving customer satisfaction and avoiding inventory wastage.

Machine Learning & Predictive Maintenance: Where to Start

In AI, data is key. To start testing the waters for Predictive Maintenance with Machine Learning, it’s crucial to evaluate the dataset available in the manufacturing facility. This includes data captured by sensors and in data historians.

The following 5 questions should be asked with regards to the facility’s dataset to ensure that Predictive Maintenance is a worthwhile endeavor at this time:

  1. Is there enough data to describe the uninterrupted manufacturing process as well as failure events? This refers to newly captured data and historical data. Take into consideration which machines are generating data, the periods of time covered by each one, the number of failures that each machine experienced during this time.
  2. What are the current data collection methods in place? And, what steps are needed to expand data collection to cover all the needed data for machine learning? For example, can machines be connected using IoT gateway devices to start capturing new data?
  3. Are there existing data sources that can be used to augment machine data? For example, work order and raw material data from the ERP, quality data from the PLM system, and maintenance logs).
  4. Who will interpret behavioral patterns? Is there an expert on-site or will external consultation be required?
  5. What is the desired business outcome? How are the benefits of Predictive Maintenance reduced downtime, improved quality, development of new products/services, etc. – expected to affect the company’s bottom line.

Predictive Maintenance & Machine Learning – A Golden Opportunity

We’re only beginning to scratch the surface, but it’s clear that the role of Machine Learning in Predictive Maintenance, and in the manufacturing sector at large, will continue to grow as more companies adopt this technology and experience its positive impact on their ROIs.