Reliability measurement and estimation of an industrial system is a difficult and essential problematic task for control engineers. In this context reliability can be described as the probability that machine network will implement its proposed functions under the observing condition throughout a specified time period of running machine system network. In this study single sensor method is applied for fault diagnosis depending on identification of single parameter. At early stages it is hard to diagnose machine fault due to ambiguities in modeling environment. Due to these uncertainties and ambiguities in modeling, decision making become difficult and lead to high financial loss. To overcome these issues between the machine fault symptoms and estimating the severity of the fault; a new method of artificial intelligence fault diagnosis based approach Dempster-Shafer theory has been proposed in this paper. This theory will help in making accurate decision of the machine condition by fusing information from different sensors. The experimental results demonstrate the efficient performance of this theory which can be easily compared between unsurpassed discrete classifiers with the single sensor source data.

In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

Estimating the industrial machine system reliability is a challenging and important problematic task for the manufacturing engineers. Reliability may be described as the probability that machine network will implement its proposed functions under the observing condition throughout a specified time period of running machine system network. Currently, typical single sensor method is common in use for fault diagnosis which based on the signature of single/multiple parameters, but due to uncertainty of modelling environment; it is difficult to diagnose the machine faults at early stages. In this situation, sometimes decision goes wrong due to uncertainty and may cause loss of throughput and significant financial losses. Fault diagnosis has to build a relation between the machine fault symptoms and estimating the severity of the fault. An artificial intelligence (AI) technique is proposed in this research for machine fault diagnosis by using the extracted features, where accurate mathematical method is difficult to build. This paper focuses on Dempster Shafer (D-S) evidence theory to diagnose the machine faults. The results reflect that the D-S theory is very effectively applied for the machine fault diagnosis and it also increase the reliability of machine operation process and decrease the uncertainty level in decision making.