Basis Pursuit based intelligent diagnosis of bearing faults

Purpose – To present a new application of Pursuit based analysis for diagnosing rolling element bearing faults. Methodology Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a comparative study of both the Basis and Matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis. Findings – Fault features were extracted from vibration acceleration signals and subsequently fed to a Feed Forward Neural Network (FFNN) for classification. The classification rate and Mean Square Error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the Basis Pursuit fault diagnosis procedure were compared with the classification result of a Matching Pursuit feature-based diagnostic procedure. The comparison clearly illustrates that Basis Pursuit featurebased fault diagnosis is significantly more accurate than Matching Pursuit feature-based fault diagnosis in detecting these faults. Practical implications Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgments on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery. Originality/Value – This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.