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This paper presents an efficient approach to estimate the failures of various components in an induction motor using motor current signature analysis. Conventional sensor-based fault detection methods lead to huge manpower and require greater number of sensors. To overcome these drawbacks, current signature base fault detection is proposed. An advanced spectral analysis, namely discrete wavelet transform (DWT), is used for frequency domain analysis. This paper also presents fault severity estimation using feature extraction-based evaluation of DWT coefficients. As the DWT gives many coefficients at higher level decomposition which is essential for high resolution, fault classification and severity index become challenging. To address this issue, artificial neural network (ANN) algorithm is used after DWT decomposition. The fault severity is predicted by proposed fault indexing parameter of various features like energy, standard deviation, skewness, variance, RMS values. Conventional algorithms like support vector machine, k‐nearest neighbour, local mean decomposition-singular value decomposition and extreme learning machine have given maximum of 98–99% accuracy, Whereas the proposed DWT-based ANN has given 100% accuracy with tanh function. Moreover, the testing loss with this function is also very less. Experimental results have affirmed the accuracy of proposed fault detection of various faults in induction motor of rating 3—Phase, 1. 5KW, 440 V and 50 Hz. [ABSTRACT FROM AUTHOR]

Background: Misalignment is one of the major reasons for rotating machinery breakdown. Conventionally, misalignment diagnosis is done by the occurrence of a strong peak at 2x running speed is widely accepted. Purpose: Both angular and parallel misalignment type display peak at 2x running speed, so it is difficult to discern the form of misalignment by using vibrational signal alone. This study addresses classification of misalignment faults (parallel, angular, and combined) by using a diagnostic medium by vibration signal and acoustic emission. Methods: The vibro-acoustic sensors are utilized to capture data with different speed and defect intensity conditions for misaligned rotor system. Time domain features are extracted, and features are ranked using t test (One-way ANOVA). Ranked features are used to train different machine learning algorithms. Results: The investigation shows the vibro-acoustic sensor data fusion as an efficacious tool for misalignment fault classification. This work also intended to carry out fault classification using a small amount of training data by employing different algorithms. Conclusion: The proposed method of misalignment fault classification provides better and reliable results than fault segregation using vibration and acoustic signals separately. [ABSTRACT FROM AUTHOR]