Multiple Faults Detection of Gas Turbine by MLP Neural Network

Abstract

This paper describes a procedure to measure the performance of detection and isolation of multiple faults in gas turbines using artificial neural network and optimization techniques. It is on a particular form of artificial neural networks, the traditional multi-layer perceptron (MLP). Error back-propagation and different activation functions are used. The main goal is to recognize single, double and triple faults in a turboshaft engine, whose performance data were output from a gas turbine simulator program, tuned to represent the engine running at an existing power station. MLP network is a nonlinear interpolation function usually made of input layer, hidden-layer and output-layer, with different neuronal units, but in this work, only one hidden-layer was used. Weights were altered by error back-propagation from the initial values established from a seed fixed between 0 and 1. The activation function in the MLP algorithm is the sigmoid function. The best moment to stop the training process and avoid the over fitting problem was chosen by cross-validation. Optimization of convergence error was achieved using the momentum criteria and reducing the oscillation problem in all nets trained. Several configurations of the neural network have been compared and evaluated, using several noise graduations incorporated to the data, aiming at finding the network most suitable to detect and isolate multiple faults in gas turbines. Based on the results obtained it is inferred that the procedure reported herein may be applied to actual systems in order to assist in maintenance programs, at least.

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

“…An ANN-based user friendly GT fault identification system was provided by Kong et al [132]. A multiple fault detection system was developed by Matuck et al [133] using this approach which is trained on simulation data. They considered single, double, and triple component faults together with sensor noise.…”

Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method

“…The advantage of this concept has been evaluated for ANNs (3,7) and recently for an SVM to some extent (15). Conversely, the multiple fault classification success rate obtained from a single MLP structure, as reported in (36), may also support the utilization of SVM in general and nested SVM modules in particular.…”

“…From this table, it can be seen that the SVM provided better classification accuracy in all the fault scenarios than the MLP method, especially with high-level measurement noise. The degree of influence of measurement noise on the classification effectiveness of an MLP is also investigated in (36). In general, on average, the classification performance of the hybrid method showed over 12 % improvement than the general MLP based scheme.…”

Gas turbine gas path diagnostics: A review

“…It is mentioned that GA and ANN methods used as modelling technique to develop an integrated engine fault diagnostic tool for identifying shifts in component performance and sensor faults [36]. Recently, a comparative study was carried out by Kong et al [48] on GPA and GA based diagnostic systems of a dual-spool turbofan engine.…”

“…The proposed tool can detect compressor fouling and is useful for an effective washing schedule. Similarly, Matuck [36] introduced MLPNN based engine diagnostics algorithm that can recognize a single, double and triple faults. Likewise, to evaluate component performance and sensor faults, hybrid diagnostic method was developed by Sampath [37] using GA and ANN techniques.…”