Benefits of Condition Monitoring

Condition monitoring enables equipment manufacturers and operators to:

• Reduce unplanned failures: Detect anomalies and faults before they become major problems.
• Optimize maintenance schedules: Avoid the costs of unnecessary maintenance by scheduling service only when necessary.
• Minimize downtime: Isolate the source of faults more quickly to reduce the time required for diagnostics and maintenance.

Condition monitoring is not just about collecting data but also about using that data to evaluate the condition of a machine. In practice, it could be anything from a control chart that ensures a single sensor value does not exceed a safety threshold to a machine learning algorithm trained on hundreds of sensors with months of historical data.

Condition Monitoring Algorithms

Condition Monitoring vs. Prognostics

A predictive maintenance program may include both condition monitoring and prognostics algorithms. The main difference between condition monitoring and prognostics is the time frame.

Condition monitoring focuses on the current state of machinery. It identifies faults and anomalies using real-time data and enables immediate corrective actions to prevent failures. Condition monitoring approaches include anomaly detection algorithms, which detect when machine behavior deviates from normal, and diagnostics algorithms, which identify specific component faults.

Prognostics, on the other hand, looks into the future to estimate the remaining useful life of equipment by analyzing trends and patterns in the data. This predictive aspect helps in planning maintenance activities well in advance, optimizing resource allocation and minimizing downtime.

Together, condition monitoring and prognostics algorithms can help form a comprehensive prognostics and health management (PHM) strategy that enhances the reliability and longevity of machinery.

Developing Condition Monitoring Algorithms in MATLAB

A typical workflow for developing condition monitoring algorithms in MATLAB^® includes acquiring and preprocessing data, identifying condition indicators, training the model, and deploying and integrating the algorithm.

Acquire Data

Acquiring data is always the first step in developing condition monitoring algorithms. If you have streaming or archived sensor data from operational machines, there are many ways to access it for analysis. You can obtain the data directly through test hardware or retrieve it by connecting to tools such as OPC UA, RESTful web services, databases, AWS S3, and Azure Blob.

It can be a challenge to acquire and organize the right data for training condition monitoring algorithms—especially data representing faults and failures. You can replace or augment existing operational data by generating synthetic data using physics-based models built in Simscape™.

Explore and Preprocess Data

Achieving a deep understanding of the data is key to designing valuable condition monitoring algorithms. This may require preprocessing data to remove outliers, noise, trends, and other artifacts. Preprocessing ensures that condition monitoring algorithms are trained on the most representative information available.

In this data exploration phase, it may be useful to visualize parts of the data set. Domain experts who understand what the data represents, may be able to detect anomalies and faults easily by eye. In this case, simple condition monitoring algorithms such as findchangepts or a control chart may be sufficient.

If the data contains many sensors and complex patterns that are not easily identifiable, more advanced methods will be required to extract meaning from the data set. This is where AI-based approaches like machine learning and deep learning become valuable.

Design Condition Monitoring Algorithms

The first step in designing condition monitoring algorithms usually involves identifying condition indicators: features that indicate the difference between normal and faulty operation. Identifying condition indicators may be straightforward or may require an iterative process of extracting and analyzing derived quantities from many sensors to find meaningful patterns. This process is known as feature engineering.

Predictive Maintenance Toolbox™ includes the Diagnostic Feature Designer app, which lets you extract, analyze, and rank the most relevant features for detecting faults interactively. This app simplifies the task of converting raw data into useful derived features, making it easier to develop effective condition monitoring algorithms.

After identifying the right condition indicators, the next step is to use them to create a condition monitoring algorithm. This involves training machine learning or deep learning algorithms, which learn from the selected features to detect faults and anomalies accurately. The Classification Learner app enables you to interactively try out different fault classification methods to find the most suitable condition monitoring algorithm via comparison of various machine learning models.

Deploy and Integrate Condition Monitoring Algorithms

To provide business value, condition monitoring algorithms need to be deployed and integrated in an operational environment such as an on-premises server or cloud. Condition monitoring algorithms can also be deployed to an embedded system, enabling faster response times and reducing the amount of data sent over the network.

Deployment involves transferring the algorithm from a development environment into a real-world operational setting where it can start monitoring equipment. This step requires careful planning to ensure that the algorithm interfaces seamlessly with the machinery’s sensors and data collection systems. Integration, on the other hand, involves embedding the algorithm within the operational workflows, ensuring that it communicates effectively with other maintenance and monitoring systems. This might include setting up notifications for maintenance teams when the algorithm detects a potential issue or integrating with a dashboard that displays real-time health indicators of the machinery.

The deployment and integration phase marks the transition from theoretical design to practical application, where the algorithm’s effectiveness in predicting and preventing equipment failures is truly tested. Condition monitoring algorithms can become key tools for predictive maintenance, helping to extend equipment life and improve operational efficiency.

For tools and examples related to condition monitoring, explore Predictive Maintenance Toolbox.