Genetic algorithm based on optimization of neural network structure for fault diagnosis of the clutch retainer mechanism of MF 285 tractor

Abstract

Introduction The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA) method was used to optimize the structure of the Artificial Neural Networks (ANN) for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor. Materials and Methods The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a data analyzer (PCA-40), a personal computer, a piezoelectric accelerometer (VMI-102, DT-2234B), a tachometer and two rubber vibration absorbing elements are located between the rig’s components and the plate holder. An evaluation function was employed in order to achieve the optimal structure of neural network models by selecting the number of layers, number of cells in the layers, transfer function, training function, learning functions, performance function, and number of epochs, in such a way that the MSE of the calculated output error was minimal. The data were collected by means of the accelerometer sensor attached on the clutch mechanism, with three different working conditions (normal condition, with worn bearing, and with worn shaft), and three rotational speeds including: 1000 rpm, 1500 rpm and 2000 rpm. The Wavelet Packet Transform (WPT) was applied on the data-set for features vector extraction and the principle component analyses (PCA) was applied for dimension reduction of the features vector. The signal processing and the features extraction are the most important characteristics of the monitoring methodology, by which the working condition of the machine can be determined. These characteristics may be acquired by transforming the signals from the time domain to the frequency domain and MATLAB software is used for this purpose. This software receives the vibration data (time series of output voltage) which are in Excel files format. To remove the noise a suitable filtering procedure was used and finally the statistical parameters of time - frequency were calculated. Results and Discussion To verify the accuracy of the Genetic Algorithm model, the required data were collected from the training and testing steps of the Neural Network. For this purpose, the statistical parameters such as mean squared error (MSE), mean absolute error (MAE) and correlation coefficient (r) were used. The optimal parameters of the neural network obtained for the family of Db4. A trial and error procedure was used to minimize the mean square error of the network output and the desired amount of training step. During the training step, four neural networks including Db4, Db30, Db35 and Db40 achieved a gradient descent weight in the learning bias and four neural networks including Db9, Db15, Db20 and Db25 achieved a gradient descent with momentum weight in the learning bias. The two of the achieved neural networks including Db4, Db20 have circular logarithm function and the remaining networks have annular hyperbolic tangent transfer function. The most appropriate networks configuration was acquired when the network exhibited the minimal error with the training and testing data sets. The results show that the highest accuracy of the GA-ANN Artificial neural networks for all rotational speeds (1000, 1500 and 2000 rpm), and working conditions (intact gear and shaft, damaged bearing and worn shaft) observed for the network family of Db4. The highest error observed for the family of Db20 with MSE of 0.011. Conclusions Artificial neural networks can somewhat think and make decisions similar to an expert person. In this project in order to predict the occurrence of a failure of the clutch mechanism of MF 285 tractor, the experimental data were obtained using some sensors, and the data were transferred to a computer by means of a data analytical. By training of the neural networks, the errors were identified separately. The output data from the combined Neural Network and Genetic Algorithm shows that the performance of the prediction model is enhanced. Based on the experiments and calculations, the best data set belongs to the family of Db4 network with the least MSE equal to 4.09E-07 and r equal to 0.99999, indicating that the model could precisely detect the faulty bearings or shafts.

Keywords

• clutch retainer mechanism
• fault diagnosis
• genetic algorithm
• neural network
• wavelet packet transforms