Achieving World-Class Predictive Maintenance with Normal Behavior Modeling
Central to the normal behavior modeling (NBM) concept is an algorithm known as an autoencoder, shown in Figure 1. Over time, the autoencoder’s input layer ingests a continuous stream of quantitative data from equipment sensors (temperature, pressure, etc.). This data is then fed to a hidden layer (of which there are typically several), where it gets compressed. Numerical weights (a value between 0 and 1) are then applied to each node, with the goal of eventually reproducing the input values at the output layer.
The principal purpose of NBM is to define the normal state of a complex system and then proactively identify instances where the system is operating outside of normal with sufficient advance warning to allow maintenance or repair actions to take place to avoid revenue loss, repair costs, and safety compromises that typically come with such failures.