Parallel quantized dual-level fully connected classifier for bearing fault diagnosis

Abstract

The diagnosis of bearing failures in rotating mechanical equipment is critical to productivity and the quality of the manufacturing process. Traditional methods generally suffer from high human factor interference and low accuracy. This paper proposes a parallel quantized dual-level fully connected classifier (PQDFCC) for fault identification of bearings. The PQDFCC has two parts. The first part of the PQDFCC contains two types of data pre-processing. The first data pre-processing method is calculating absolute values and sorting the acquired raw signals. The second method of data pre-processing is quantizing the raw data. The second part of PQDFCC is a dual-level fully connected classifier. The first-level fully connected classifier performs the first-level fault classification on the quantized and sorted data; then, the output probability of the first-level classifier is considered as the input of the second-level fully connected classifier; finally, the second-level fully connected classifier provides the classification results of bearing faults. The data pre-processing part of the PQDFCC is a simple calculation that can simplify and not lose fault information; the dual-level fully connected classifier can fully exploit the hidden features of the data with high accuracy. The proposed PQDFCC is tested in the bearing failure, axial piston hydraulic pump, and self-priming centrifugal pump datasets; the experimental results show that the PQDFCC achieves 100% fault diagnosis accuracy.