Anfis-Based Defect Severity Prediction on a Multi-Stage Gearbox Operating Under Fluctuating Speeds

Previous research investigators have exploited machine-learning algorithms to diagnose the defects in rotating machinery. However, with increasing complexity in the design of rotating machinery, it is quite challenging to quantify the faults precisely. In this present study, an attempt has been made to predict the defect severity of the rotating machinery using Adaptive Neuro-Fuzzy Inference System (ANFIS). This ANFIS algorithm employs artificial neural networks to define the membership functions, rules and weights to construct the fuzzy inference system. Experiments are performed on a multi-stage spur gearbox model while it is subjected to fluctuating operating speeds. Two local defects on bearing race as well as on gear tooth with four different severity levels are seeded intentionally. Three condition monitoring (CM) strategies, namely, vibration, lubrication oil and acoustic signal analyses are executed, and the raw data is recorded synchronously. The raw vibration and acoustic waveforms are decomposed through discrete wavelet transform to extract the descriptive statistics from the wavelet coefficients. Among them, most discriminating features are selected and given as input to ANFIS classification tool to train the network for obtaining the Sugeno-type FIS, which in turn estimates the severity of the component. Later, the features from the individual CM strategies are combined to devise an integrated feature dataset which is further channelled as input to the ANFIS for predicting the defect severity levels. The investigation reveals that, the proposed integrated feature set in conjunction with ANFIS can discriminate between the defect severity conditions of the gears as well as bearings under fluctuating speeds.