Orthogonal binary singular value decomposition method for automated windshield wiper fault detection

For automobile manufacturers, reducing vehicle interior noise is essential for increasing customer satisfaction and vehicle quality. Windshield wipers are one of the major components that generate such noises, and faulty wipers could negatively affect passengers' psychological and physiological perceptions while driving. Thus, identifying faulty wipers during the manufacturing process would improve the driving experience and vehicle and road safety as well as reduce driver distraction. However, the existing windshield wiper noise-detection process is entirely manual, relies upon human subjectivity, and is time-consuming. Accordingly, this paper develops a novel automated windshield wiper fault-detection system. First, a novel binarization approach is used to effectively binarize the transformed spectrograms of sound signals from windshield wiper operation to segment nAoisy regions. Then, a new matrix-factorisation approach called orthogonal binary singular value decomposition is proposed to decompose binarized mel spectrograms into uncorrelated binary eigenimages to extract meaningful features and identify faulty wipers. Then, the k-nearest neighbour classifier is utilised to classify the extracted features into normal or faulty windshield wipers. Finally, to demonstrate the effectiveness of the proposed system, it was validated on real-life windshield wiper reversal and squeal noise datasets, where it outperformed existing methods and achieved accuracies of 95% and 94%, respectively.

Automated summary

For automobile manufacturers, reducing vehicle interior noise is crucial for improving customer satisfaction. This paper proposes a novel automated windshield wiper fault-detection system that outperforms existing methods in accurately identifying faulty wipers. By utilizing a binarization approach and matrix-factorization technique, meaningful features are extracted from sound signals to classify the wiper's condition.