Defect state and Severity Analysis Using the Discretized State Vectors

Abstract

The time series of sensor data for condition monitoring of a system is often characterized as very-short, intermittent, transient, highly nonlinear and non-stationary random signals, which hinders the straightforward pattern analysis. For discovering meaningful features from original sensor data, we transform continuous time series data into a set of contiguous discretized state vectors using a multivariate discretization approach. We then search for important patterns that are found only in the case of defective systems. We discuss how to measure the level of importance of each defect pattern and further how to assess the severity degree of a defective state. We consider that a defective state is more severe if various defect patterns are observed in the state. Likewise, if a particular defect pattern describes as many as defective states, the pattern will be treated as significant. The proposed procedure is applied to detecting defective car door trims that have the potential to generate small but irritating noises. We analyzed the datasets obtained from two different monitoring methods using a typical acoustic sensor array and acoustic emission sensors. Defective car door trims were efficiently identified with their severity degrees.