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Fault Diagnosis and Application Based on SOM Neural Network
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SOM neural network is a fully connected array of neurons composed of non-teachers and self-learning network, which has a strong nonlinear mapping ability and flexible network structure and a high degree of fault tolerance and robustness. This paper introduces the structure of SOM neural network and learning algorithm and presents an instance of marine diesel engines in MATLAB environment. The diagnosis of marine diesel engine showed that the model can reduce the cost of diagnosis and increase the efficiency of diagnosis. There will be well application prospect in practice.
Rolling bearings are common parts in the transmission systems and have been widely used in various kinds of applications. The normal operation of the rolling bearings hence plays an important role on the efficiency of the system performance. However, due to hostile working environment the rolling bearings are prone to failures. The transmission systems may break down when there occurs faults in the rolling bearings. As a result, it is essential to detect the faults of rolling bearings. However, when use artificial intelligence method to diagnose the rolling bearings faults the signal processing is extensively complex while very few works have been done on the simplification of the artificial neural network (ANN) models for the rolling bearings fault detection. To deal with this problem, a simple self-organized map (SOM) neural network method together with a principal component analysis (PCA) based feature reduction procedure is proposed to diagnosis rolling bearings faults in this work. The vibration data of the normal and faulty rolling bearings was acquired from an experimental test bed. The PCA was firstly used to extract distinct fault features. Then the SOM was employed to train and learn the fault features to identify the fault patterns. The fault detection results show that the proposed method is feasible and effective for the fault diagnosis of rolling bearings. The fault detection rate is beyond 89.0%.
The article discusses how to generate RGB images with noise using Kohonen’s self-organizing map (SOM). The article also describes the adaptation process and structure of SOM, which can be used to generate RGB images with noise. The authors of the article evaluate the influence of SOM parameters (a learning coefficient, adaptation time, effective width) on the noise level of RGB image generated using SOM. According to these observations, the authors formulate several recommendations how to control the noise level by adjusting SOM parameters.
For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.
This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps (SOM) as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test.
The major issue of open switch fault diagnosis in Voltage Source Inverters is false alarm generated as a result of load and frequency variations. The main objective of this paper is to solve such an issue by extracting minimum number of features from fault detection parameter. The fault diagnostic system (FDS) under variable load conditions requires more number of features to be extracted from detection parameter. Therefore stator currents are taken in the DQ coordinate that is Park’s Vector Transform (PVT). The PVT is used to normalize the currents without affecting nature of transients caused due to fault occurrence. The normalized currents are passed through Discrete Wavelet Transform (DWT) and features are extracted from detail coefficients of DWT under healthy and faulty conditions. As a result of normalized currents, the extracted features of three phase currents are same under different load conditions but have definite distinctive values under different faulty conditions. Hence, once features are extracted for single load conditions they remain same for all load conditions. An Artificial Neural Network is trained using these features. The results are presented for different fault configurations, single and multiple switch faults under variable load conditions at different frequencies. Additionally, the results are presented for the real-time diagnostic of faults, showing the instance of fault occurrence and the instance of fault isolation.
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