Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.

To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.

Microscopic fringe projection profilometry (MFPP) technology is widely used in 3D measurement. The measurement precision performed by the MFPP system is closely related to the calibration accuracy. However, owing to the shallow depth of field, calibration in MFPP is frequently influenced by low-quality target images, which would generate inaccurate features and calibration parameter estimates. To alleviate the problem, this paper proposes an unsupervised-learning-based calibration robust to defocus and noise, which could effectively enhance the image quality and increase calibration accuracy. In this method, first, an unsupervised image deblurring network (UIDNet) is developed to recover a sharp target image from the deteriorated one. Free from capturing strictly paired images by a specific vision system or generating the dataset by simulation, the unsupervised deep learning framework can learn more accurate features from the multi-quality target dataset of convenient image acquisition. Second, multi-perceptual loss and Fourier frequency loss are introduced into the UIDNet to improve the training performance. Third, a robust calibration compensation strategy based on 2D discrete Fourier transform is also developed to evaluate the image quality and improve the detection accuracy of the reference feature centers for fine calibration. The relevant experiments demonstrate that the proposed calibration method can achieve superior performance in terms of calibration accuracy and measurement precision.