Abstract
Machinery health monitoring techniques provide valuable insights into the performance and condition of machines. Acoustic sensor-based monitoring has emerged as a significant area of interest for the industry due to its ability to accurately capture fault signatures, thereby improving the detection accuracies of anomalies or deviations from regular operations. However, the collected sensor signals typically contain a complex mixture of sounds that relate to multiple fault conditions, environmental noise, and other unwanted sounds from the surroundings. Identifying the specific root causes of failures is a challenge in modeling without knowledge of the unique characteristics of failure conditions. This can ultimately degrade the model’s performance or yield inaccurate failure estimations in condition monitoring, which is a consistent concern in the industry. Therefore, this study proposes a novel framework that enhances the accuracy of machinery fault diagnosis using audio source separation of complex mixture of sound signals. The proposed approach employs a Deep Extractor for Music Source Separation (DEMUCS), a state-of-the-art music source separation approach consisting of an encoder-decoder architecture that uses bi-directional long-short-term memory (LSTM) for industrial machine sound separation and enhancement. The proposed methodology comprises two steps. In the first step, the fault sound isolation and recovering individual fault sounds from a complex mixture of sound signals are enabled using DEMUCS. In the second step, the isolated fault sounds are fed through a 1D-convolutional neural network (1D-CNN) classifier for adequate classification. A machine fault simulator by Spectra Quest equipped with a condenser mic was employed to evaluate the proposed DEMUCS-CNN methodology for identifying multiple faults. The effectiveness of the DEMUCS-CNN method was also compared to the traditional approach of blind source separation (BSS). The outcomes of the comparison indicated that the suggested approach of fault isolation by DEMUCS led to enhanced fault classification accuracy, making it a more effective approach compared to conventional BSS.