Auto-identification of engine fault acoustic signal through inverse trigonometric instantaneous frequency analysis

The acoustic signals of internal combustion engines contain valuable information about the condition of engines. These signals can be used to detect incipient faults in engines. However, these signals are complex and composed of a faulty component and other noise signals of background. As such, engine conditions’ characteristics are difficult to extract through wavelet transformation and acoustic emission techniques. In this study, an instantaneous frequency analysis method was proposed. A new time–frequency model was constructed using a fixed amplitude and a variable cycle sine function to fit adjacent points gradually from a time domain signal. The instantaneous frequency corresponds to single value at any time. This study also introduced instantaneous frequency calculation on the basis of an inverse trigonometric fitting method at any time. The mean value of all local maximum values was then considered to identify the engine condition automatically. Results revealed that the mean of local maximum values under faulty conditions differs from the normal mean. An experiment case was also conducted to illustrate the availability of the proposed method. Using the proposed time–frequency model, we can identify engine condition and determine abnormal sound produced by faulty engines.