Predictive maintenance (also known as PHM or equipment “health monitoring”) refers to the intelligent monitoring of equipment to avoid future equipment failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms using data collected from equipment sensors.
Predictive maintenance offers the following benefits for customers and equipment manufacturers:
Algorithms are critical to predictive maintenance success. Data in the form of temperature, pressure, voltage, noise, or vibration measurements is collected using dedicated sensors. It is processed using various statistical and signal processing techniques. This data is then used to monitor the health of the equipment by comparing it against the established markers of faulty conditions using data clustering and classification, or other machine learning techniques. In a model-based approach, this data can also be used to build predictive models of the system’s behavior for condition-monitoring. This model is then employed to track changes in the equipment’s condition and predict its remaining useful life.
Once tested, predictive maintenance algorithms may be operationalized in an IT environment such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.
See also: Parallel Computing Toolbox, Signal Processing Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, Neural Network Toolbox, MATLAB, Database Toolbox, data analytics, unsupervised learning, predictive modeling, prescriptive analytics