Fault diagnosis in refrigerators using fan-induced acoustic signals

Official URL: https://risc01.sabanciuniv.edu/record=b2953263_(Table of contents)

Detection of the source of the fault is an important issue in industrial products. According to the analyses regarding refrigerators, it has been determined that the majority of customer complaints are caused by noise-based complaints. Therefore, it is very important to identify the main source causing the noise problem and correct it as fast as possible. The aim of this thesis is to classify fan-related faults in refrigerators using sound signals. The method applied to diagnose the source causing the fault was preferred to be data-based, and for this reason, it was aimed to carry out the study with the help of a suitable algorithm that learns from the dataset. Creating a reliable and detailed dataset in order to improve the data infrastructure for use in this thesis and future studies is the secondary aim of the study. In this thesis, in the case of only one of the 3 fan sources of the refrigerator is faulty and all of them are working properly, a sound dataset is created by acquiring sound data in ISO 3745 compliant full anechoic measurement environment. An ensemble classification model is proposed by using a machine learning model trained by extracting the statistical features of the sound signal and a CNN (Convolutional Neural Network) architecture trained using mel spectrograms, which are the visual representation of the sound signal. The proposed model classifies with an accuracy of 93% when the non-faulty class is not included and 89% when the non-faulty class is included.