Condition Monitoring, Anomaly Detection, and Predictive Maintenance – What is it, and why should you use it?

I will start by describing the electric motors in general. Electric motors are essential components of many industrial and commercial systems. Electric motors are used in many industries, from large manufacturing plants to transportation, healthcare, and household appliances. These applications, as we can imagine, depend heavily on the smooth, efficient operation of electric engines, which are integral components in these systems. As the demand for productivity and efficiency continues to increase, it is becoming increasingly important to maintain these motors. Electric motors may experience a variety of issues that could impact their efficiency, performance, and lifespan. Three critical practices are needed to ensure safe and efficient electric motor-driven application operation: anomaly detection and condition monitoring.

What is the difference between these critical practices or techniques?

First, let’s define our terminology:

Anomaly Detection – is the process of identifying deviations or deviations from patterns of behavior. Electric motors can cause anomalies to manifest themselves as sudden changes in the operating conditions. This could be abnormal vibrations or temperature spikes. These changes may indicate underlying issues, such as worn-out or faulty bearings or excessive power consumption.

Condition Monitoring is the process of continuously collecting and analyzing information on electric motors’ health and performance. By monitoring motor performance indicators like temperature, vibration, and power consumption on a regular basis, ML models (machine learning) can detect subtle changes that could indicate developing problems. This information allows maintenance technicians to take preventative actions, such as cleaning, lubrication, or repairs before a problem escalates.

Predictive maintenance – goes beyond condition monitoring by using advanced analytics, machine learning algorithms, and predicting when maintenance is needed.

Predictive maintenance systems detect anomalies by comparing real-time sensor data to historical motor performance. They can also predict the failure of critical components, such as bearings and shafts. This information allows maintenance teams to schedule repairs and replacements in advance, reducing downtime while maximizing motor lifespan.

What’s required to use one of these techniques?

First, we must identify and understand the states of the system that need to be monitored. Second, we must identify the best data to use to detect these states. Data and data analysis are the third step. We can analyze the data to classify patterns, values, and conditions. This allows us to differentiate between normal and abnormal behavior and define the anomaly with the best-fit machine learning model.

Ingredients needed for this recipe:

1. Sensors – Depending on the application, sensors are required to measure vibration, temperature, and other parameters.
2. Data acquisition system: Data from sensors needs to be collected and stored on a cloud platform or database. Data acquisition systems are used to store, analyze, and process sensor data. RealityCheck ™ Motor Toolbox has been developed specifically for this purpose. Figure 1 shows a typical block diagram of RealityCheck Motor in the context of the data flow and process.
3. Data processing, analysis, and ML model creation – We now need to analyze sensor data in order to identify any anomalies or patterns which may indicate a potential issue. RealityAI tools(r) will automatically select the model based on the results of data analysis.

For more information and examples of how our machine learning software can be used to analyze your data, visit the Reality AI Software page.

But I have to reduce BoM, and that will save me money. What about a sensorless approach?

Yes, it is. In this context, a sensor-less approach may provide added benefits, especially for applications without sensors for monitoring performance parameters. Sensor-less methods use ML models that estimate motor performance based upon other data, such as voltage or current draw, which is already used by the motor control algorithm. Watch the Predictive Electric Motor Maintenance video for more information about how and why Reality AI Tools can be used with the RealityCheck Motor Toolbox.

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Figure 1: Renesas Development and Data Path System Block Diagram for anomaly detection and condition monitoring

What’s up with RealityCheck Motor?

RealityCheck Motor add-on toolbox enables anomaly identification, conditional monitoring, and predictive maintenance without the need for additional sensors. The motor’s electrical signals and parameters can be used as a proxy to simulate other sensors. RealityCheck Motor uses readily available information to collect minute changes in the parameters of the system that indicate anomalies or maintenance issues. It’s designed to integrate seamlessly with Renesas motor-driven applications, MCUs and MPUs. This allows for hardware optimization and the creation of machine-learning algorithms. This toolbox and Reality AI Tools offer a low-code automated machine learning platform that will enable you to create, validate, and deploy sensor classification or predictive models in your targeted Renesas devices.

RealityCheck Motor is the ideal toolbox for optimizing motor systems through machine learning to ensure maximum uptime and efficiency.

Electric motors are an essential part of many commercial and industrial applications. Their efficient operation is crucial to maintaining productivity and cutting costs. Businesses can extend the life of their equipment by implementing strategies such as anomaly detection, condition tracking, and predictive maintenance. This will reduce the risk of costly repairs, downtime, and expensive repairs. See how RealityCheck Motor simplifies the process of motor control algorithm creation.