Heath monitoring of a glass transfer robot in the mass production line of liquid crystal display using abnormal operating sounds based on wavelet packet transform and artificial neural network

This paper presents the fault detect method of a moving transfer robot in the mass production line of liquid crystal display (LCD) manufacturers based on the wavelet packet transform (WPT) for feature extraction and the artificial neural network (ANN) for fault classification. Most of fault detection methods in a mechanical system have been researched based on the vibration signal. Unlike the existing methodologies, this study aims to minimize the uncertainty of a field engineer's decision making process for determining whether a fault is present or not based on the human auditory perception by developing a fault diagnosis system that uses the abnormal operating sound radiated from a moving transfer robot as a source signal. Abnormal operating sound radiated from a moving transfer robot has been used for this work instead of other source signals such as vibration, acoustic emission, electrical signal, etc. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone despite a relatively low sensitivity. In the application of ANN, since it is important to minimize the error of trained ANN in terms of the accuracy of fault diagnosis logic, in the paper, the number of input and target data samples was increased through a regeneration process based on statistical properties, and then the uncorrelated nodes in the input vector were also removed to improve the orthogonality of the input vector based on the entropy based feature selection method. Consequently, it can be concluded that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.