Jian-Da Wu

An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network

In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition. In the preprocessing of sound emission signals, WPT coefficients are used for evaluating their entropy and treated as the features to distinguish the fault conditions. Obviously, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the wavelets are used as mother wavelets to build and perform the proposed WPT technique. In the classification, to verify the effect of the proposed generalized regression neural network (GRNN) in fault diagnosis, a conventional back-propagation network (BPN) is compared with a GRNN network. The experimental results showed the proposed system achieved an average classification accuracy of over 95% for various engine working conditions.

Driver identification using finger-vein patterns with Radon transform and neural network

A driver identification system using finger-vein technology and an artificial neural network is presented in this paper. The principle of the proposed system is based on the function of near infra-red finger-vein patterns for biometric authentication. Finger-vein patterns are required by transmitting near infra-red through a finger and capturing the image with an infra-red CCD camera. The algorithm of the proposed system consists of a combination of feature extraction using Radon transform and classification using the neural network technique. The Radon transform can concentrate the information of an image in a few high-valued coefficients in the transformed domain. The neural networks are used to develop the training and testing modules. The artificial neural network techniques using radial basis function network and probabilistic neural network are proposed to develop a driver identification system. The experimental results indicated the proposed system performs well for personal identification. The average identification rate of PNN network is over 99.2%. The details of the image processing technique and the characteristic of system are also described in this paper.

Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.

Finger-vein pattern identification using principal component analysis and the neural network technique

Research highlights? Presents a personal identification system using finger-vein patterns. ? Develops an identification system based on neural network. ? Finger-vein patterns feature extraction using principal component analysis. ? Patterns classification using back-propagation network and adaptive neuro-fuzzy inference. This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.

A forecasting system for car fuel consumption using a radial basis function neural network

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.

Development of an expert system for fault diagnosis in scooter engine platform using fuzzy-logic inference

In the present study, a fault diagnosis system using acoustic emission with an adaptive order tracking technique and fuzzy-logic interference for a scooter platform is described. Order tracking of acoustic or vibration signal is a well-known technique that can be used for fault diagnosis of rotating machinery. Unfortunately, most of the conventional order-tracking methods are primarily based on Fourier analysis with the revolution of the machinery. Thus, the frequency smearing effect often arises in some critical conditions. In the present study, the order tracking problem is treated as the tracking of frequency-varying bandpass signals and the order amplitudes can be calculated with high resolution. The order amplitude figures are then used for creating the data bank in the proposed intelligent fault diagnosis system. A fuzzy-logic inference is proposed to develop the diagnostic rules of the data base in the present fault diagnosis system. The experimental works are carried to evaluate the effect of the proposed system for fault diagnosis in a scooter platform under various operation conditions. The experimental results indicated that the proposed expert system is effective for increasing accuracy in fault diagnosis of scooters.

Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference

This paper described a development of the fault gear identification system using the vibration signal with discrete wavelet transform and fuzzy-logic inference for a gear-set experimental platform. The proposed system consisted of a combination of signal feature extraction using discrete wavelet transform technique and fault identification using fuzzy-logic inference. Traditionally, the technique for fault diagnosis in rotating machinery depends on the experience of the technician. However, the rotating machinery may be operated in a complex and noisy environment. The conventional diagnosis technique has difficulty detecting the fault features, such as in a noisy environment. In the present study, a discrete wavelet transform technique using vibration signals in a gear-set experimental platform is studied. The extraction method of feature vector is based on discrete wavelet transform with energy spectrum. Further, the fuzzy-logic inference is proposed to develop the diagnostic rules of the data base in the present fault identification system. The experimental works are performed to evaluate the effect of fault diagnosis in a gear-set platform under various operation conditions. The experimental results indicated the proposed expert system is effective for increasing accuracy in fault gear identification of the gear-set platform.

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.