Fuzzy logic based classification of faults in mechanical differential

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential

Artificial neural network based classification of faults in centrifugal water pump

The detection and diagnosis of faults are of great practical significance for the safe operation of a plant. Early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. This paper presents the design and development of ANN-based model for the fault detection of centrifugal water pump using a back-propagation learning algorithm and multi-layer perceptron neural network. The centrifugal pump conditions were considered to be healthy pump and faulty impeller and faulty seal and cavitation, which were four neurons of output layer with the aim of fault detection and identification. Features vector, which is one of the most significant parameters to design an appropriate neural network, was extracted from analysis of vibration signals in frequency domain by means of FFT method. The statistical features of vibration signals such as mean, standard deviation, variance, skewness and kurtosis were used as input to ANN. Different neural network structures are analyzed to determine the optimal neural network with regards to the number of hidden layers. The results indicate that the designed system is capable of classifying records with 100 % accuracy with one hidden layer of neurons in the neural network

Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals

Gearboxes are widely applied in power transmission lines, so their health monitoring has a great impact in industrial applications. In the present study, acoustic signals of Pride gearbox in different conditions, namely, healthy, worn first gear and broken second gear are collected by a microphone. Discrete wavelet transform (DWT) is applied to process the signals. Decomposition is made using Daubichies-5 wavelet with five levels. In order to identify the various conditions of the gearbox, artificial neural network (ANN) is used in decision-making stage. The results indicate that this method allow identification at a 90 % level of efficiency. Therefore, the proposed approach can be reliably applied to gearbox fault detection

Artificial neural network based classification of faults in centrifugal water pump

The detection and diagnosis of faults are of great practical significance for the safe operation of a plant. Early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. This paper presents the design and development of ANN-based model for the fault detection of centrifugal water pump using a back-propagation learning algorithm and multi-layer perceptron neural network. The centrifugal pump conditions were considered to be healthy pump and faulty impeller and faulty seal and cavitation, which were four neurons of output layer with the aim of fault detection and identification. Features vector, which is one of the most significant parameters to design an appropriate neural network, was extracted from analysis of vibration signals in frequency domain by means of FFT method. The statistical features of vibration signals such as mean, standard deviation, variance, skewness and kurtosis were used as input to ANN. Different neural network structures are analyzed to determine the optimal neural network with regards to the number of hidden layers. The results indicate that the designed system is capable of classifying records with 100 % accuracy with one hidden layer of neurons in the neural network

Corresponding Author Comparison of Artificial Neural Networks ANN and Statistica in Daily Flow Forecasting 1

ABSTRACT Two decades with the advent of methods based on artificial intelligence and genetics. Algorithm directly based upon different parameters to predict water engineering is highly developed Accurate prediction of flow in rivers, always as one of the most important factors in safe and economic design of facilities and structures related to river water has been considered by researchers .In this study, the method of artificial neural network and statistica model, are used to forecast daily river flow in north of Iran and the results of these models are compared with Observed daily values. The observed data that are used in this study start from 1992 to 2010 in18 year's period (6550 days). The study of two sigmoid functions and tangent hyperbolic MLP model was used to train and eventually compared with the results of STATISTICA software, the results showed that the ability of the neural network model output better than the statistical softwar