Time-frequency diagnosis, condition monitoring, and fault detection

Abstract

This chapter aims to further illustrate the (t,f) approach by selecting a few key generic applications of diagnosis and monitoring. The topic is represented by seven sections.

One key application is electrical power quality and the presence of transient disturbances. To detect and assess their effect on voltage and current stability, we can use the instantaneous frequency (IF) as an estimator of disturbance propagation (Section 15.1). In the automotive industry, the treatment and prevention of knock is a major problem for internal combustion engines as car spark knocks caused by an abnormal combustion may lead to engine damage. The Wigner-Ville distribution is used to optimize the position for placement of knock sensors (Section 15.2). Other applications involve signals that have dispersive spectral delays governed by a power law, such as dispersive propagation of a shock wave in a steel beam and cetacean mammal whistles. A power class of TFDs suitable for such applications is formulated and a methodology is described (Section 15.3). In applications of image processing, image quality may be assessed using a 2D-WVD based measure correlated with subjective human evaluations. It is shown that this SNR measure based on the WVD outperforms conventional SNR measures (Section 15.4). Some general principles of (t,f) diagnosis are then reviewed for medical applications with focus on heart sound abnormality diagnosis (Section 15.5). For machine condition monitoring, a task crucial to the competitiveness of a wide range of industries, the tasks of detecting and diagnosing faults in machines, is made easier using machine learning methods with (t,f) approaches such as the WVD, wavelets, and wavelet packets (Section 15.6). The last specific example is the condition monitoring of assets using (t,f) methods with focus on the prevention of steel beam damage (Section 15.7).