ICA Based Sensors Fault Diagnosis: An Audio Separation Application

Abstract

Independent component analysis (ICA) is a well known technique of blind source separation (BSS) and is used in various applications, e.g. speech, biomedical, communication, robotics, leakage detection, vibration analysis and machinery fault diagnosis. The ICA technique estimates the original source signals from the recorded multidimensional mixed signals through various sensors from any physical process. In case one or more sensor becomes faulty the separation of mixed signals becomes very difficult. In machinery, ICA is used to diagnose faults in its rotating parts that is a major concern of public safety. In case of faulty sensors, fault diagnosis becomes difficult. Moreover, in certain situations of wireless sensor networks some of the sensors fail to collect accurate information. Therefore, to collect accurate information in case of sensor failure, fault diagnosis technique is required. In this paper, a sensor fault diagnosis technique called the state observing technique (SOT) is developed to first diagnose faults in the system and then identify the faulty sensors. Also the extended sensor technique (EST) is developed to improve the separation performance of the ICA algorithm in case of faulty sensors based on information provided by the SOT technique. Effectiveness of the proposed SOT–EST technique is evaluated through extensive simulations utilizing the FastICA algorithm of ICA. To the best of our knowledge, we are the first to discuss the sensor fault diagnosis in the ICA applications.