Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s poor convergence commonly associated with the LM algorithm. Specifically, the effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) and Parametric Rectified Linear Unit (PRLU) were evaluated on the general performance of LM neural networks, and special values of LM neural network parameters were found that could make the LM algorithm converge poorly. We proposed an adaptive LM (AdaLM) algorithm to solve the problem of the LM algorithm. The algorithm coordinates the descent direction and the descent step by the iteration number, which can prevent falling into the local minimum value and avoid the influence of the parameter state of LM neural networks. We compared the AdaLM algorithm with the traditional LM algorithm and its variants in terms of accuracy and speed in the context of testing common datasets and aero-engine data, and the results verified the effectiveness of the AdaLM algorithm.

Airlines evaluate the energy-saving and emission reduction effect of washing aeroengines by analyzing the exhaust gas temperature margin (EGTM) data of aeroengines so as to formulate a reasonable washing schedule. The noise in EGTM data must be reduced because they interfere with the analysis. EGTM data will show several step changes after cleaning the aeroengine. These step changes increase the difficulty of denoising because they will be smoothed in the denoising. A denoising method for aeroengine data based on a hybrid model is proposed to meet the needs of accurately evaluating the washing effect. Specifically, the aeroengine data is first decomposed into several components by time and frequency. The amplitude of the component containing the most noise is amplified, and Gaussian noise is added to generate noise-amplified data. Second, a Gated Recurrent Unit Autoencoder (GAE) model is proposed to capture engine data features. The GAE is trained to reconstruct the original data from the amplified noise data to develop its noise reduction ability. The experimental results show that, compared with the current popular algorithms, the proposed denoising method can achieve a better denoising effect, retaining the key characteristics of the aeroengine data.