Pattern Classification by an Incremental Learning Fuzzy Neural Network

To detect and identify defects in machine condition health monitoring, classical neural classifiers, such as Multilayer Perceptron (MLP) neural networks, are proposed to supervise the monitored system. A drawback of classical neural classifiers, off-line and iterative learning algorithms, is a long training time. In addition, they are often stuck at local minima, unable to achieve the optimum solution. Furthennore, in an operating mode, it is possible that new faults are developing while a monitored system is running. These new classes of defects need to be instantly detected and distinguished from those that have been trained to the classifier. Those classical neural classifiers need to be retrained by both old and new patterns in order to learn new patterns without forgetting the learned patterns. Conventional classifiers cannot detect and learn the new fault types on-line real-time. Using incremental learning algorithms in the monitoring system it is possible to detect those new defects of machine conditions with the system operating while maintaining oLd knowledge. Inspired by the promising properties of an incremental learning algorithm named Fuzzy ARTMAP Neural Network, a new algorithm suitable for pattern classification based on fuzzy neural networks called an Incremental Learning Fuzzy Neuron Network (ILFN) is developed. The ILFN uses Gaussian neurons to represent the distributions of the input space, while the fuzzy ARTMAP neural network uses hyperboxes. The ILFN employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized classifier with the capability of adaptive learning of new information without forgetting old knowledge. The classifier can detect new classes of patterns and update its parameters while in an operating mode. Moreover, it is an on-line (real-time) and fast learning algorithm without knowing a priori information. In addition, it has the capability to make soft (fuzzy) and hard (crisp) decisions, and.it is able to classify both linear separable and nonlinear separable problems. To prove the concept, simulations have been performed with the vibration data known as the Westland Data Set. This data set was obtained from the Internet at http://wisdom.ar1.psu.edulWestland/ collected from U.S. Navy CH-46E helicopters maintained by Applied Research Laboratory (ARL) at Penn State University. Using a simple Fast Fourier Transform (FFT) technique for the feature extraction part, the network, capable of one-pass, on-line, and incremental learning performed quite well. Training by various torque levels, the network achieved 100% correct prediction for the same torque level of testing data. Furthermore, the classification performance of the network has been tested using other benchmark data, such as the Fisher's Iris data, the two-spiral problem, and a vowel data set. Comparison studies among other well-known classifiers were preformed. The ILFN was found competitive with or even superior to many classifiers

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda

Credit scoring models for Egyptian banks : neural nets and genetic programming versus conventional techniques

Credit scoring has been regarded as a core appraisal tool of banks during the last few decades, and has been widely investigated in the area of finance, in general, and banking sectors, in particular. In this thesis, the main aims and objectives are: to identify the currently used techniques in the Egyptian banking credit evaluation process; and to build credit scoring models to evaluate personal bank loans. In addition, the subsidiary aims are to evaluate the impact of sample proportion selection on the Predictive capability of both advanced scoring techniques and conventional scoring techniques, for both public banks and a private banking case-study; and to determine the key characteristics that affect the personal loans' quality (default risk). The stages of the research comprised: firstly, an investigative phase, including an early pilot study, structured interviews and a questionnaire; and secondly, an evaluative phase, including an analysis of two different data-sets from the Egyptian private and public banks applying average correct classification rates and estimated misclassification costs as criteria. Both advanced scoring techniques, namely, neural nets (probabilistic neural nets and multi-layer feed-forward nets) and genetic programming, and conventional techniques, namely, a weight of evidence measure, multiple discriminant analysis, probit analysis and logistic regression were used to evaluate credit default risk in Egyptian banks. In addition, an analysis of the data-sets using Kohonen maps was undertaken to provide additional visual insights into cluster groupings. From the investigative stage, it was found that all public and the vast majority of private banks in Egypt are using judgemental approaches in their credit evaluation. From the evaluative stage, clear distinctions between the conventional techniques and the advanced techniques were found for the private banking case-study; and the advanced scoring techniques (such as powerful neural nets and genetic programming) were superior to the conventional techniques for the public sector banks. Concurrent loans from other banks and guarantees by the corporate employer of the loan applicant, which have not been used in other reported studies, are identified as key variables and recommended in the specific environment chosen, namely Egypt. Other variables, such as a feasibility study and the Central Bank of Egypt report also play a contributory role in affecting the loan quality.The Egyptian Governmen