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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.

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This report introduces the concept of three-dimensional (3D) radial plots for the visualization of multivariate large scale datasets in plant operations. A key concept of this representation of data is the introduction of time as the third dimension in a two dimensional radial plot, which allows for the display of time series data in any number of process variables. This report shows the ability of 3D radial plots to conduct systemic fault detection and classification in chemical processes through the use of confidence ellipses, which capture the desired operating region of process variables during a defined period of steady-state operation. Principal component analysis (PCA) is incorporated into the method to reduce multivariate interactions and the dimensionality of the data. The method is applied to two case studies with systemic faults present (compressor surge and column flooding) as well as data obtained from the Tennessee Eastman simulator, which contained localized faults. Fault classification using the interior angles of the radial plots is also demonstrated in the paper. / text

<p>The main topic of this licentiate thesis is fast faultdetection. The thesis summaries the work performed in theproject“Fast fault detection for distributionsystems”.</p><p>In the first chapters of the thesis the term“fast”is used in a general manner. The term is laterdefined based upon considerations and conclusions made in thefirst chapters and then related to a specific time.</p><p>To be able to understand and appreciate why fast faultdetection is necessary, power system faults and theirconsequences are briefly discussed. The consequences of a faultare dependent of a number of different factors, one of thefactors being the duration of the fault.</p><p>The importance of the speed of the fault detection dependson the type of equipment used to clear the fault. A circuitbreaker which interrupt currents only when they pass through anatural zero crossing might be less dependent on the speed ofthe fault detection than a fault current limiter which limitsthe fault current before it has reached its first prospectivecurrent peak.</p><p>In order to be able to detect a fault in a power system, thepower system must be observed, i.e., measurements of relevantquantities must be performed so that the fault detectionequipment can obtain information of the state of the system.The fault detection equipment and some general methods of faultdetection are briefly described.</p><p>Some algorithms and their possible adaptation to fast faultdetection are described. A common principle of many algorithmsare that they assume that either a signal or the power systemobject can be described by a model. Sampled data values arethen fitted to the model so that an estimate of relevantparameters needed for fault detection is obtained. An algorithmwhich do not fit samples to a model but use instantaneouscurrent values for fault detection is also described andevaluated.</p><p>Since the exact state of a power system never is known dueto variations in power production and load, a model of thepower system or of the signal can never be perfect, i.e., theestimated parameter can never be truly correct. Furthermore,errors from the data acquisition system contribute to the totalerror of the estimated parameter.</p><p>Two case studies are used to study the performance of the(modified) algorithms. For those studies it has been shown thatthe algorithms can detect a fault within approximately 1msafter fault inception and that one of the algorithms candiscriminate between a fault and two types of common powersystem transients (capacitor and transformer energization).</p><p>The second case study introduced a system with two sourceswhich required a directional algorithm to discriminate betweenfaults inside or outside the protection zone.</p><p>It is concluded that under certain assumptions it ispossible to detect power system faults within approximately 1msand that it is possible to discriminate a power system faultfrom power system transient that regularly occurs within powersystems but which not are faults.</p>