Accurate rotor position is essential for permanent magnet synchronous motor (PMSM) to execute vector control. Due to the big volume, the resolver and encoder as standard position sensors cannot be used in the space limited application. The sensor-less methods whose characteristic are sensitive to the parameter of motor and cannot be used in mass-produce. Therefore, this paper presents tunnel magnetoresistance (TMR) sensors as sensing element embedded into the PMSM to detect the magnetic field applied by the built-in magnets and high frequency time pulses served as a measurement standard to measure the position. TMR sensors excited by the altering voltage are integrated with the PMSM without taking up extra space. Besides, the rotation position is measured by counting the time pulses corresponding to the phase shift of the TMR sensor’s output signal, which is simple and effective method to detect the position. Relate design aspects include corresponding signal circuit and sensing element is detail introduced in the paper. Experiment results prove the validity of the proposed method and multi-group TMR sensors are used to get higher detection accuracy. Although developed for the PMSM, the presented embedded position detecting approach can be used for where the space is limited or the other types of actuators to make it integration.

Unsupervised domain adaption (DA) is a well-established technique for fault diagnosis of rotating machinery, which has attracted considerable attention in recent years. However, existing DA methods assume that the label spaces of the source domain and target domain are consistent, and this assumption is not always satisfied in industrial settings as new fault types would inevitably occur during operation. Aiming at this issue, an open-set fault diagnosis (OSFD) network is proposed for rotating machinery, denoted as the target domain slanted adversarial network (TDSAN). Specifically, two significant innovations are incorporated. Firstly, a target domain slanted classifier (TDSC) is developed to tackle the biased learning problem by leveraging the target domain data distribution. Secondly, an adaptive threshold for unknown fault identification is designed to enhance the distinguishability between known and unknown faults in the target domain. Finally, to evaluate the effectiveness and robustness of the proposed TDSAN, extensive experiments were conducted on two fault datasets: a bearing fault dataset and a gearbox fault dataset. The ablation study was also performed to validate the contributions of each innovation of the proposed TDSAN, and the experimental results demonstrated the superiority of the proposed framework.

The success of fault diagnosis based on deep learning benefits from a large amount of labeled fault samples. However, the scarcity of labeled fault samples in fault diagnosis of wind turbine gearbox (WTG) makes it difficult to train a satisfactory diagnostic model. To address this issue, this article proposed a semisupervised weighted centroid prototype network (SSWCPN) for WTG fault diagnosis. Specifically, SSWCPN is a few-shot semisupervised learning framework, which alleviates the matter of overfitting caused by the lack of supervision information. First, to capture abundant semisupervised information to guide network training, a sample selection model based on the evolution trend of posterior probability is proposed, which could efficiently cherry-pick out the unlabeled samples of high confidence to refine prototypes. Second, a new prototype updating strategy based on a weighted centroid prototype is designed, which controls the prototype drifting issue caused by incorrect pseudolabels and the introduction of new data distribution. Finally, experiments performed on test-bench data and successful application on WTG data show that the proposed SSWCPN-based WTG fault diagnosis achieves the best fault diagnosis performance among the comparison methods.

Fault diagnosis under specific working conditions has achieved remarkable success. However, due to variations in working conditions, the assumption that training and test samples are independent and identically distributed is often violated, which makes the diagnostic model brittle under unseen working conditions. To this end, a generic generalization strategy, namely, regularized domain adaptive weight optimization strategy (RDAWOs), is devised for fault diagnosis of rotating machinery. We first design the architecture of a 1-D convolutional neural network. Then, the hyperparameter regularization term and an adaptive pooling layer are designed to control the complexity and improve the adaptability of the overparameterized deep model, respectively. Finally, domain adaptive weight optimization is established to identify the working condition abundant in spurious label-related information and to mine the robust fault knowledge under various working conditions. Obtained results indicate the strong generalization ability for out-of-distribution samples, as well as relatively high diagnostic accuracy of the RDAWOs-based deep model under unseen working conditions.