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Interturn short-circuit fault (ISF), insulation fault (INF), eccentricity fault (ECF), and demagnetization fault (DMF) are the most common faults in permanent magnet synchronous motors (PMSMs). As these faults degrade their reliability, an integrated fault diagnosis method that can diagnose multiple faults is necessary. Fault indicator (FI) is the basis of fault diagnosis. In current methods, a single FI makes it difficult to diagnose the above four faults. In this article, an FI composed of stator tooth flux (STF) with multiple teeth is proposed. The fault features extracted by deep learning networks from the proposed FI can diagnose the four investigated faults. First, the STF of multiple teeth under four faults is analyzed to understand the effects of these faults on flux at different positions. Then, the FI is constructed based on analyzed characteristics. The proposed FI exhibits excellent performance in relatively few STFs (different teeth). Finally, the effectiveness and generality of the proposed FI are verified by a deep learning network based on simulation data and experimental data. The proposed FI provides a significant reference for the diagnosis of multiple faults in PMSM.

This article investigates the leader-following formation control problem of nonholonomic agents with a persistently exciting leader based on bearing measurements. A follower needs to maintain a desired relative position to the leader in its local coordinate frame using bearing measurements. An integrated method consisting of a bearing-based estimator and a tracking controller is proposed to achieve the leader-following control objective. The estimator is designed to estimate the relative position to the leader in the follower's local coordinate frame. Based on the estimated relative position, the controller is proposed to drive the follower to achieve the desired formation. Different from existing integrated methods, our method does not require any conditions on the relative bearing between the leader and the follower in the global frame, which are difficult to guarantee. We characterize the conditions locally on the leader only, namely, conditions on the leader's linear and angular velocities with persistently exciting steering speed, such that the overall system is asymptotically stable with our integrated method. Besides, the proposed method is extended to the multiagent formation control problem. A reasonable solution is proposed to the multiagent case with a minimal leader–follower structure. Simulations prove the effectiveness of the proposed methods in both cases.

Fiber-optic Fabry–Perot interference (FPI) sensors offer remarkable advantages in sensing applications, particularly in harsh environments, while errors from measurement and demodulation can deteriorate the sensing precision. To better understand the error origin, a systematic error model based on First Principles is established, including random and bias errors. An optimized cavity length demodulation (CLD) method, based on the neglected imperfect incidence spectrum, is proposed for cavity length extraction and spectrum decomposition. Demodulation experiments with a fixed cavity length were conducted on the graphene fiber-optic FPI sensor. The validity of the error model and optimized CLD (opt-CLD) method was verified. The random error mainly originates from the random wander of interference peak around the resolution of the spectrum analyzer, while the bias error mainly originates from the imperfect incidence spectrum. The graphene FPI sensor and opt-CLD method were applied to pressure sensing. The bias error was effectively eliminated, and the achieved random error was 20 times lower than that of the peak–peak demodulation (P-P) method. These findings in this article may provide valuable insights and solutions for the FPI sensor applications.

The need for cancelable biometric techniques has seen a progressive rise due to the rapid deployment of biometric authentication systems. These techniques prevent compromising biometric data by generating and using their corresponding cancelable templates for user authentication. However, the non-invertible distance preserving transformation methods employed in various schemes are often vulnerable to information leakage since matching is performed in the transform domain. This paper proposed a non-invertible distance preserving scheme based on vector permutation and shift-order process. First, the dimension of feature vectors is reduced using kernelized principal component analysis before randomly permuting the extracted vector features. A shift-order process is then applied to the generated features to achieve non-invertibility and combat similarity correlation-based attacks. The generated hash codes are resilient to various security and privacy attacks such as ARM, masquerade, and brute-force preimage. Experimental evaluations conducted on eight fingerprint datasets from FVC2002, FVC2004, and FVC2006 reveal a high matching performance of the proposed method with better recognition accuracy than other existing state-of-the-art. The scheme also fulfills the revocability and unlinkability requirements of cancelable biometrics.