Acoustic Diagnostics of Electrical Origin Fault Modes with Readily Available Consumer-Grade Sensors

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This paper presents low-cost real-time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer-grade sensors for low-cost real-time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the PWM-controller of a BrushLess DC motor. Experimental trials positively detect 99.9 % of the 1160 resultant high-bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components

Bearing Fault Diagnosis Using Feature Ranking Methods and Fault Identification Algorithms

AbstractDiagnoses of bearing faults are important to avoid catastrophic failures in rotating machines. This paper presents a methodology to detect various bearing faults from the measured vibration signal. Features such as kurtosis, skewness, mean, root mean square and complexity measure such as Shannon entropy are calculated from time domain,frequency domain and discrete wavelet transform. In total 40 features are calculated from bearing conditions such as Healthy bearing, Inner race fault, Outer race fault and Ball fault. Feature ranking methods such as Chisquare, ReliefF method are used to select most informative feature and subsequently to reduce size of feature vector. Comparison has been made between feature ranking methods and classifiers to obtain best diagnosis result with reduce feature set. Our results shows good fault identification accuracy with minimum number of features