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No description available.

No description available.

No description available.

No description available.

Automated fault diagnosis in induction machines is a difficult task and normally requires background information of electrical machines. Here a different methodology to the condition monitoring problem is devised. The approach is based entirely on Digital Signal Processing (DSP) and Statistical Pattern Recognition (PR). Description of machine conditions is extracted from empirical data. The main tasks that must be carried out by a PR-based condition monitoring system are: condition identification, knowledge reinforcement and knowledge creation for previously unseen conditions. The DSP operations are employed to quickly isolate sensor faults and to remove noise using data acquired from a single channel. DSP transformations may seem promising in making the monitoring system portable. Most importantly, they can compensate for operational changes in the machine. These changes affect the supply line currents and the primary signal quantities to be measured, i.e. the current and the axial leakage flux. The data which is input to the statistical monitoring system may be transformed, in the form of features, or remain unaltered. The system exploits the statistical properties of the feature vectors. The particular features, namely the LAR coefficients, convey short-term, high-resolution spectral information. For a long record, the feature vector sequence may provide information about changes in the record spectral characteristics, with time. Many induction machine processes are stationary and they can be properly be dealt with by a simple statistical classifier, e.g. a Gaussian model. For nonstationary processes, the system may employ a more comprehensive tool, namely the Hidden Markov Model. which may track the changing behaviour of the process in question. Initially a limited number of machine conditions are available to the process engineer. By identifying their boundaries, new faulty conditions could be signalled for and adopted into the database.

In this study, an efficient new hybrid approach for multiple sensors data fusion and fault detection is presented, addressing the problem with possible multiple faults, which is based on conventional fuzzy soft clustering and artificial immune system (AIS). The proposed hybrid system approach consists of three main phases. In the first phase signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently a single (fused) signal based on the information provided from the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations and experiments for multiple sensor systems have confirmed the strength of the new approach for online fusing and fault detection. The hybrid system gives a fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the new hybrid approach attractive for solving such fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical systems based on a set of extracted features; these features characterize the collected sensors data. The hybrid system is able to detect the onset of fault conditions which can lead to critical damage or failure. This early detection of failure signs can provide more effective information for any maintenance actions or corrective procedure decisions.

A "low cost fault detection system" that identifies wheel flats and defective tracks is explored here. This is achieved with the conjunction of sensors, microcontrollers and Radio Frequency (RF) transceivers. The objective of the proposed research is to identify faults plaguing railcars and to be able to clearly distinguish the faults of a railcar from the inherent faults in the track. The focus of the research though, is mainly to identify wheel flats and defective tracks. The thesis has been written with the premise that the results from the simulation software GENSYS are close to the real time data that would have been obtained from an actual railcar. Based on the results of GENSYS, a suitable algorithm is written that helps segregate a fault in a railcar from a defect in a track. The above code is implemented using hardware including microcontrollers, accelerometers, RF transceivers and a real time monitor. An enclosure houses the system completely, so that it is ready for application in a real environment. This also involves selection of suitable hardware so that there is a uniform source of power supply that reduces the cost and assists in building a robust system.

No description available.

No description available.