An adaptive lifting scheme and its application in rolling bearing fault diagnosis

Vibration signals of rolling bearings usually are corrupted by heavy noise and it is very important to extract fault features from such signals. In this paper, an adaptive lifting scheme is proposed for fault diagnosis of rolling bearings. The kurtosis indexes of scale decomposition signals are used as the optimization indicator to select the prediction operator and update operator, which can adapt to the dominant signal characteristics, and reveal the fault feature. Fourier transform is adopted to remove the overlapping signal frequency components at every scale decomposition signal. Experimental results confirm the advantage of the adaptive lifting scheme over lifting scheme for feature extraction, and the typical features of rolling bearing in time domain are successfully extracted by adaptive lifting scheme

An optimal lifting multiwavelet for rotating machinery fault detection

The vibration signals acquired from rotating machinery are often complex, and fault features are masked by background noise. Feature extraction and denoising are the key for rotating machinery fault detection, and advanced signal processing method is needed to analyze such vibration signals. In this paper, an optimal lifting multiwavelet denoising method is developed for rotating machinery fault detection. Minimum energy entropy is used as the metric optimize the lifting multiwavelet coefficients, and the optimal lifting multiwavelet is constructed to capture the vibration signal characteristics. The improved denoising threshod method is used to remove the background noise. The proposed method is applied to turbine generator and rolling bearing fault detection to verify the effectiveness. The results show that the method is a robust approach to reveal the impulses from background noise, and it performs well for rotating machinery fault detection

Incipient defect identification in rolling bearings using adaptive lifting scheme packet

Defects on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. Defect detection in rolling bearings via techniques that examine changes in measured signal is a very important topic of research due to increasing demands for quality and reliability. In this paper, incipient defect identification method based on adaptive lifting scheme packet is proposed. Adaptive lifting scheme packet operators which adapt to the signal characteristic are constructed. The shock pulse value in defect sensitive frequency band is used as the defect indicator to identify the defect location and severity of rolling bearing. The proposed defect identification method is applied to analyze the experimental signal from rolling bearing with incipient inner raceway defect. The result confirms that the proposed method is accurate and robust in rolling bearing incipient defect identification

Robust occupancy inference with commodity WiFi

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. in this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity wifi infrastructure. depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. we evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness

Rolling bearing fault identification using multilayer deep learning convolutional neural network

The vibration signal of rolling bearing is usually complex and the useful fault information is hidden in the background noise, therefore, it is a challenge to identify rolling bearing faults from the complex vibration environment. In this paper, a novel multilayer deep learning convolutional neural network (CNN) method to identify rolling bearing fault is proposed. Firstly, in order to avoid the influence of different characteristics of the input data on the identification accuracy, a normalization preprocessing method is applied to preprocess the vibration signals of rolling bearings. Secondly, a multilayer CNN based on deep learning is designed in this paper to improve the fault identification accuracy of rolling bearing. Simulation data and experimental data analysis results show that the proposed method has better performance than SVM method and ANN method without any manual feature extractor design