Hybrid intelligence for enhanced fault detection and diagnosis for industrial gas turbine engine

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

“…of a helicopter TE, carried out using the model [28], demonstrates significant differences compared to the results obtained in this work. It should be noted that the results obtained in this work highlight the critical importance of the relations under research, emphasizing its importance for optimizing engine operation and helicopter TE ensuring safe operation, at a time when the results obtained using [28] look less significant from a practical point of view applications. This is because the data presented in this work describes in detail the influence of the gas-generator rotor r.p.m.…”

“…The closest approach to the method of neural network approximation of helicopter TE parameters proposed in this work for researching the dependence of engine power on the gas-generator rotor r.p.m. for efficient use of energy in helicopter TE are the results of research [28], the main focus of which is the development of an engine hybrid model for detecting faults and diagnosing, which uses principal component analysis and artificial neural networks to improve diagnostic accuracy and efficiency for early fault detection. Condition monitoring and diagnostics not only help in engine detecting faults but also maximize energy use to ensure proper performance and minimize energy loss due to faults and sub-optimal operation.…”

“…To compare the proposed helicopter TE parameters neural network approximation method based on a three-layer forward propagation neural network with linear neurons in the output layer, trained by a modified SCG algorithm, with the closest analogue [28], which is based on hybrid fault detection and diagnosis, which consists of four main stages: data collection, data evaluation (analysis and normalization), data fusion (only for the supervised training method) and a neural network model-a three-layer perceptron trained by the classical backpropagation algorithm. Applying the training sample (Table 1) to the model [28], a diagram was constructed of the dependence of the energy change on the gas-generator rotor r.p.m. of the helicopter TE (Figure 12, where the "black curve" is a diagram constructed using the helicopter TE parameters neural network approximation method proposed in the helicopters work, that is, similar to Figure 11, the "red curve" is a diagram constructed using the model [28]).…”

“…Applying the training sample (Table 1) to the model [28], a diagram was constructed of the dependence of the energy change on the gas-generator rotor r.p.m. of the helicopter TE (Figure 12, where the "black curve" is a diagram constructed using the helicopter TE parameters neural network approximation method proposed in the helicopters work, that is, similar to Figure 11, the "red curve" is a diagram constructed using the model [28]). A comparative analysis showed that the use of the proposed modified SCG algorithm for training a neural network reduces errors of the first and second types compared with the traditional SCG algorithm by 1.35…1.61 times, the backpropagation algorithm-by 1.97…2.49 times, the quick propagation algorithm-by 2.25…2.61 times, by the quasi-Newton algorithm-by 2.65…3.12 times, the Levenberg-Marquardt algorithm-by 2.43…3.28 times.…”

“…Applying the training sample (Table 1) to the model [28], a diagram was constructed of the dependence of the energy change on the gas-generator rotor r.p.m. of the helicopter TE (Figure 12, where the "black curve" is a diagram constructed using the helicopter TE parameters neural network approximation method proposed in the helicopters work, that is, similar to Figure 11, the "red curve" is a diagram constructed using the model [28]). Research of the dependence of energy changes on the gas-generator rotor r.p.m.…”