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A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability. [ABSTRACT FROM AUTHOR]

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%. [ABSTRACT FROM AUTHOR]

Recent works suggest that using knowledge transfer strategies to tackle cross-domain diagnosis problems is promising for achieving engineering diagnosis. This article presents a diagnosis scheme for rolling bearing under a challenging domain generalization scenario, in which more potential discrepancies among multiple source domains are eliminated and only normal samples of the target domain are available during the training stage. To achieve sufficient generalization performance, a diagnosis scheme combining some a priori diagnosis knowledge and a deep domain generalization network for fault diagnosis (DDGFD) is elaborated. Through signal preprocessing steps guided by the a priori diagnosis knowledge, the inputs of DDGFD with a primary consistent meaning across domains are constructed from the vibration signal. On this basis, DDGFD would intently release its talent on learning discriminative and domain-invariant fault features from source domains, and then generalize the learned knowledge to identify unseen target samples. On cross-domain tasks organized using broad bearing data sets, the superiority of DDGFD is validated by comparing its performance with various data-driven diagnosis methods. [ABSTRACT FROM AUTHOR]

• A new intelligent transfer diagnosis method of rotating machinery, TLPPIFI, is proposed. • TLPPIFI can build the diagnosis model using historical data from other same-type machines. • TLPPIFI performs superior transfer capacity with inadequate information of target domains. • Three case studies show the validity and superiority of TLPPIFI. Intelligent fault diagnosis methods have been widely developed in recent years due to the ability in learning diagnosis knowledge from monitoring data automatically. However, for many diagnosis methods based on traditional machine learning algorithms, how to collect massive data under the same distribution with test data is a difficult problem in real world industrial applications. Aiming at this data dilemma of conventional intelligent diagnosis methods, this paper proposes a T ransfer L ocality P reserving P rojection based I ntelligent F ault I dentification (TLPPIFI) method, which can construct diagnosis model using historical data collected from different operating conditions or other same-type machines. Based on a relevance assumption, TLPPIFI first embeds the data to a subspace through preserving a priori distribution structure properties of training data and minimizing the distribution discrepancy between different datasets simultaneously. By this means, the samples with same category in different datasets could cluster together in the new space. Finally, a classifier is trained to identify the condition of target machine by the historical data and the normal data of target machine together. The effectiveness of the proposed method is validated by three real-life diagnosis cases. The experimental results demonstrate that TLPPIFI can achieve superior diagnosis performance than several supervised learning methods and transfer learning methods. In addition, empirical analysis about distribution distance between domains and parameter sensitivity are also investigated. [ABSTRACT FROM AUTHOR]