Development of an algorithm for squeak and rattle identification
Creators: name: POGORILYI, Oleksandr
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 401701
subject: Acoustics and noise control (excl. architectural acoustics)

description: Squeaks and rattles (S&amp;R) are unpleasant noises that are radiated by the internal elements of a vehicle during driving. Vehicle designers, dealer technicians and automotive scientists have been paying special attention to this undesirable phenomenon. Due to the fact that vehicles contain a lot of various structural units and elements, imperfections of their alignment and fixation have a high possibility to appear. Those imperfections lead to loss of contact between surfaces in parts of a vehicle and during driving they result in the unpredictable vibration. When the natural frequency of a loose element coincides with the frequency of the moving vehicle, a resonance occurs which results in rapid amplification of the vibration. This excessive vibration of the loose elements leads to the interaction between them. From this interaction, the unpleasant sounds of S&amp;R are radiated. Since the time when vehicles became publicly available, engineers have been working on developing techniques for detection, identification, elimination and prevention of S&amp;R noises. For these tasks, vehicle manufacturers have separate independent departments that deal with S&amp;R type of sounds. Such a department includes offices, laboratories and even proving grounds to tackle the problem of S&amp;R noise presence. Even though in a few last decades the process of diagnosing vehicles with S&amp;R noises has improved and been partially automated, the S&amp;R testing routine still takes a significant length of time, budget and specialists who have knowledge in the area. Recently, several academic groups around the world have developed a few methods for automating the testing of S&amp;R noises, however there is no widely accepted method of identification S&amp;R noises. The S&amp;R specialists who work for a vehicle manufacturer in most of the cases of testing the unpleasant vehicle interior noise, rely on their experience to identify it. From the experience of working with defective vehicles, the specialists know the pattern of radiated S&amp;R sound and can predict the location of the noise. The aim of this research is to automate the routine of identification S&amp;R sounds that the noise specialists undergo during testing defective vehicles by using audio recognition methods. Recent developments in the area of environmental sound recognition show that sound patterns can be recognized with high accuracy using convenient techniques from the signal processing and the machine learning disciplines. Therefore, in this study, a newly designed algorithm for S&amp;R noise identification is proposed. An audio fingerprinting method, known as the landmark-based audio fingerprinting system, was investigated to determine its ability to identify a given audio sample of an S&amp;R issue by matching its data to the data in a database of S&amp;R audio samples accumulated earlier. The findings show that the method requires a modification to successfully distinguish among the audio patterns of S&amp;R type of sounds. In addition, the method has limitation in size of the database of the reference S&amp;R audio samples. It was discovered that the method performs with high accuracy when a limited number of samples is present in the database. Considering this limitation, the method of Machine Learning for the classification of data was investigated. The radial basis function neural network and the support vector machine were considered as a possible classifier for S&amp;R noise. The findings show that the radial basis function neural network provides the higher accuracy in predicting the class of a tested S&amp;R audio sample when the Mel frequency cepstral coefficients are extracted as the audio data are the best descriptors of S&amp;R type of sounds. Consequently, the proposed algorithm uses machine learning techniques in combination with audio fingerprinting to identify S&amp;R sounds when a limited database of S&amp;R audio samples is available.