In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.

This paper studies the secrecy performance of an intelligent reflecting surface (IRS)-aided indoor wireless communication where the IRS is capable of adjusting the direction and phase shift of reflected signal on its surface and assists a source to communicate with an authenticated user in the presence of several unauthenticated users, which can be potential eavesdroppers. The goal of this paper is to design a tile-allocation-and-phase-shift-adjustment (TAaPSA) strategy for the IRS to optimize the average secrecy rate (ASR); moreover, the respective secrecy outage probability (SOP) for this TAaPSA is evaluated. To achieve this goal, the ray model and the Rice distribution are adopted to describe the propagation of the IRS's reflected signals and the fading process, respectively. Closed-form analytical expressions for the ASR and the SOP are derived. Using these analytical results, a genetic algorithm (GA) is utilized to find an optimal TAaPSA strategy for the IRS. The accuracy of the analytical results and the improvement in ASR using GA-based TAaPSA strategy are verified by simulation results.

Highlights • Bearing faults in the low-speed bearing system are hard to detect with the original EMD algorithm as well as the envelopeanalysis.• By considering the energy of the IMF, the proposed adaptive EMD algorithm works well in bearing fault detection and performs better than the original EMD algorithm.

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