Neural Network Fault Diagnosis System Research Articles

The Fault Diagnosis of Diesel Fuel Supply System Based on BP Neural Network Optimized by Genetic Algorithm

As an important power source of diesel engine, the fuel supply system has the characteristics of high failure rate and being difficult to be detected, which is critical to detect and diagnose the fault for the reliable and stable operation of diesel engine. In the paper, principal component analysis (PCA) was used to process the initial data, back-propagation (BP) neural network was used to diagnose fault of diesel engine, BP diagnosis efficacy was analyzed, then the BP neural network fault diagnosis system was optimized by being introduced in the genetic algorithm , and the comparison results of training error and diagnostic analysis showed that: the fault diagnosis of BP neural network optimized by genetic algorithm is more accurate than that of traditional BP, which can effectively improve the fault diagnosis accuracy and reduce the training time.

Study on Computer Network Application Layer Fault Diagnosis Based on RSNN

To solve the problem of fault diagnose of computer network application layer, the design of a neural network fault diagnosis system based on rough sets becomes possible. Details the structure of system, the structure of the rough set neural network and training method , and the effectiveness of the proposed method is validated by computer simulation.

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

Application of periodic time-varying autoregressive models to the detection of bearing faults

Model-based approaches to vibration monitoring can provide a means of detecting machine faults even if data are only available from the machine in its normal condition. The cyclostationary nature of rotating machine vibrations can be exploited by using periodic time-varying autoregressive models to model the signal better than time-invariant models. Experimental data collected from a small rotating machine set subjected to several bearing faults were used to compare time-varying and time-invariant models. Comparison is also made with a simple feature-based neural network fault diagnosis system.

Neural Network Fault Diagnosis System for a Diesel-Electric Locomotive's Closed Loop Excitation Control System

In closed loop control systems fault isolation becomes extremely difficult in the case of feedbacks being oscillatory due to corrupted signals or malfunctions in actuators. This paper investigates and highlights the development of an off-line fault detection and isolation system for the isolation of faults, which cause oscillatory conditions on a General Electric (GE) Diesel-Electric Locomotive's excitation control system. The paper illustrates the use of artificial neural networks as a replacement to classical analytical models used for residual generation. The artificial neural network model's design is based on model-based dedicated observer theory to isolate sensor, as well as component faults, where observer theory is utilised to effectively select input-output data configurations for detection of sensor and component faults causing oscillations. Residual Evaluation is done with the use of a moving average filter incorporated with the simple thresholding technique. The results indicated 100% accuracy for the detection and isolation of the component or sensor responsible for causing excessive oscillation in the excitation control system.