Performing maintenance that is reactive and not proactive is costly and labor-intensive. This type of maintenance, whether it be time- or count-based, results in low machine performance, premature part replacement, early machine deterioration, and unscheduled downtime.

Predictive Maintenance Analytics allows us to push the boundaries of production efficiency and reach a goal that until now has seemed unachievable – zero unplanned downtime.

To do this, an Industry 4.0 Predictive Maintenance system needs to be able to collect and store relevant data, and analyze it in order to drive actionable business outcomes. This is the true goal of Predictive Maintenance.

Predictive Maintenance Analytics vs reactive maintenance

How to Improve Asset Efficiency Using Predictive Maintenance Analytics

Historical Data

The first step in getting started with predictive analytics is to consolidate all the relevant raw data. Before setting out to capture newly created machine data, it’s important to collect and organize as much historical information as possible.

Every machine in the production line should be taken into consideration for this phase as this historical data will be extremely valuable when combined with the new data that is currently being collected and processed via Historians, QA, ERP, and other systems.

Current/New Data

Collecting a specified and accurate set of data is central to the idea of the smart factory, and to Predictive Maintenance in particular.

Improvements in technology have enabled the development of sensors that are reliable, non-intrusive, inexpensive, and simple to integrate into a complete Industrial IoT system.

Some common sensor types used in industry include:

  • Pressure sensors
  • Temperature sensors
  • Optical sensors
  • Water quality/condition sensors (pH, conductivity, turbidity etc.)
  • Chemical sensors
  • Gas sensors
  • Smoke sensors
  • Level sensors (liquid and solid materials)
  • Accelerometers
  • Gyroscope sensors
  • Humidity sensors

Data Transport and Analysis

To securely extract the data from the sensors, IoT connectivity protocols such as MQTT are used, enabling the information to be sent via a gateway device to a designated cloud.

Organizing and aggregating the data results in a formatted dataset that can be subjected to a variety of analytical models. The result is an accurate behavioral visualization of every machine and process in the operation.

For Predictive Maintenance, there are two main types of data that are relevant – Static and Temporal.

Static Data refers to the technical specifications and operational attributes of a particular machine. These machine features are inherently static, but if they do have the ability to change over time, they need to have timestamps associated with them.

Temporal Data refers to live operational telemetry, the current condition of the machine, its work order log, usage history, and failure and maintenance history.

Once the data is in the cloud, algorithms are applied to recognize patterns and generate insights in the form of dashboards and alerts, providing valuable insights for reducing downtime and improving product efficiency.

Data Transport and Predictive Maintenance Analysis

There are numerous predictive analytics methods, ranging from linear regression and time series models to survival analysis and geospatial predictive modeling.

These methods can be organized into 2 main categories:

1. Regression – this set of PdM analysis techniques focuses on building a mathematical equation as a model that represents interactions between the various elements in a machine’s process.

For example, using a Linear Regression model, the relationship between the response (dependent) variable and a group of predictor (independent) variables could be analyzed and expressed as an equation that predicts the response variable as a linear function of all the influential elements. This model can be further optimized by minimizing the size of the residual, and testing to see that it’s distributed randomly with respect to the predictions that the model proposes.

For cases where the output (response variable) is discrete instances and not continuous, a Discrete Choice model can be used instead of multiple instances of the Linear Regression model mentioned above.

Another analytical method that falls under the Regression category is Logistic Regression which suits cases where the dependent variable is binary and we’d like to transform this data into an unbound continuous variable, estimating a common multivariate model.

To construct predictions, Likelihood-ratio and Wald tests are used to measure the statistical significance of each coefficient b in the model. Finally, to assess the goodness-of-fit of the classification model being used, a “Percentage Correctly Predicted” test is used.

2. Machine Learning – PdM analysis techniques based on Machine Learning have the ability to predict dependent variables directly, without needing to focus on the relationships between the variables. This can be very useful in scenarios where defining the dependencies between variables is extremely complex.

Machine Learning techniques emulate characteristics of human cognition and learn by “training” with example problems, building the ability to predict future events.

Leveraging Predictive Maintenance Analytics

The analytical processes mentioned above give birth to Predictive Models that can help us determine the optimum time to perform maintenance, and the type of service needed, before an asset has dropped below its performance threshold.

Because a PdM model is based upon actual machine behavior, equipment failures of any kind can be predicted, making this technology a game-changer for manufacturers. Repairs are more focused and secondary damage is prevented since technicians know the exact type of issue to prepare for, making unplanned downtime a non-issue.