Influence of entropy inclusion in multi-feature fusion for optimizing multi-component fault detection of rotational machinery under variable speed

Abstract Most of the research studies are focused on detecting dynamic changes in non-linear time series with the entropy-based methods and intensifying the fault diagnostic performance in rotational machines. However, the investigations in multi-signal fusion and multi-component fault detection with reduced computation complexity are still unexplored with the assistance of entropy-focused feature-level fusion. Therefore, the paper proposes multi-feature fusion of three reputed entropy measures with instantaneous frequency (IF) and two signal envelopes (lower and upper envelope) of vibration and sound signals to identify bearing, gear, and shaft faults in rotational machinery under speed variation. To begin with, concatenated IF and amplitude demodulated features are fed into three machine learning algorithms (SVM-RBF, DT, and ANN), and this yields moderate results. Recognizing the significance of entropy in quantifying the non-linear systems, Shannon entropy, permutation entropy, and Renyi entropy is coupled with IF and the envelope of time-varying signals for enriching the model assessment. But in consideration of redundant feature removal, the Random Forest (RF) algorithm is utilized to rank these attributes. Prudently ordered IF, Shannon entropy, and lower envelope are more effectuated as three prevailing signal traits in identifying multiple faults with 93.11% accuracy of SVM-RBF. This model is extended by embedding it with BILSTM as a hybrid model, which results in 94.45%. Though this appears to be a desirable outcome, but accounting for the decision-making runtime, BILSTM-CNN with faster computation time of 0.106 seconds, is preferred to be the best fit that delivers 96.36% in potentially recognizing multi-component faults under variable speed condition.