Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

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Mechanical structures, such as pressure vessels and pipes, need careful inspection and monitoring to avoid serious corrosion failures. Detecting and identifying corrosion damages from acoustic emission (AE) signals is of substantial importance for the safety and reliability of engineering structures in structural health monitoring. The identification accuracy depends largely on how well of damage features are being used. This paper presents a new approach for extracting effective damage features and accurately identifying different damages from AE signals during corrosion monitoring. Specifically, the proposed approach combines ensemble empirical mode decomposition (EEMD) and linear discriminant analysis (LDA) to analyze the AE signals generated from intergranular corrosion process. The results show that three damage modes including the environmental noises, the intergranular corrosion, and the formation and propagation of cracks, are successfully detected and identified from complicated AE waveforms. The proposed approach is capable of providing a reliable, direct and visualized corrosion damage detection and identification in structural health monitoring. Results from this study will guide complementary efforts aimed to detect and identify different damages from AE signals, and provide supporting knowledge regarding the industrial application of AE monitoring.

Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission (AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform (XWT) and wavelet coherence (WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.