To ensure the safety and continuity of production, make a reasonable maintenance plan, and save the cost of maintenance for a hydraulic tube tester, it is needed to quickly identify an assignable cause of a fault. This paper is concerned with early fault detection of shuttle valves, which are the key components in hydraulic tube tester. An intermittent-chaos-and-cepstrum-analysis-based method is proposed to detect this early fault on the hydraulic tube tester. The presented approach is based on the insight that the phase transition of Duffing oscillator is very sensitive to a periodic weak signal having tiny angular frequency difference with the reference signal in this oscillator. Thus, to determine the angular frequency of the reference signal, cepstrum analysis is used to determine the eigenfrequency of the fault signal, and then, the angular frequency of the referential signal is computed according to this eigenfrequency. Finally, a Lyapunov exponent is introduced to realize computer observation of the shuttle valve early fault. The presented method is experimented with data simulated from an AMESim model of hydraulic tube tester. The results indicate that the proposed approach is capable of detecting the signal of shuttle valve early fault in the hydraulic tube tester.

According to the one‐dimensional characteristics of the vibration signal, this paper proposes an elevator operation fault monitoring method based on one‐dimensional convolutional neural network (1‐DCNN). It can solve the problems of traditional elevator fault monitoring methods that require complex feature extraction processes and a large amount of diagnostic experience. Because the elevator fault monitoring field has less fault information, it is different from the large sample situation in the field of face recognition. Aiming at the problem of small samples, this paper first preprocesses elevator vibration signals through singular value decomposition (SVD) and wavelet transform, then uses wavelet transform to extract wavelet energy features of the original vibration signals, and then use PCA to reduce the feature data to the dimension with a cumulative contribution rate of greater than 85%. When reducing the dimensionality, the original characteristics of the features are preserved as much as possible. When designing the 1‐CNN, the K‐fold cross‐validation method is added to obtain as many abnormalities from the sample set as possible. The information is finally trained using the 1‐CNN and classified by softmax regression. In order to verify the performance of the algorithm, the original vibration signal was used as the input of the 1‐CNN, and the wavelet energy feature without PCA dimensionality reduction was used as the input of the 1‐CNN. The experimental results showed that the 1‐DCNN model with PCA dimension‐reduced feature data as input can effectively extract and identify the features of normal and abnormal states and has high fault identification accuracy, and good results have been obtained.

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.