Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

Deep learning and its applications to machine health monitoring

A review on empirical mode decomposition in fault diagnosis of rotating machinery

Remaining useful life (RUL) prediction of machinery is a major task in condition-based maintenance, which is able to provide crucial guidance for preventive maintenance To guarantee the accuracy for the RUL prediction of machinery subjected to two-phase degradation process, the interactive multiple model (IMM) filtering technique has been used because of its capability in estimating the state and the phase dynamically However, there are two limitations in the IMM based methods 1) A crucial parameter of the IMM, ie, the transition probabilities matrix (TPM) of the IMM, is set manually in existing IMM based methods, which often leads to inaccurate state estimation results 2) The phase estimation is derived as one-step filtering results without considering the overall evolution of the degradation trend, which is unable to describe the phase transition, thus causing inaccurate phase estimation results To tackle these two limitations, an improved RUL prediction method is proposed in this paper for machinery subjected to two-phase degradation process In the proposed method, a two-phase degradation model is constructed to describe the degradation process A nonlinear IMM technique, ie, the interactive multiple model particle filter (IMMPF) is utilized for the state and the phase estimation, where the TPM is estimated using the numerical-integration TPM estimation (NI-TPME) algorithm instead of being pre-specified manually The transition point (TP) distribution is adopted to reflect the overall evolution of the degradation trend, and is further used to modify the phase estimation from the IMMPF Finally, the RUL is predicted by Monte Carlo simulation The effectiveness of the proposed method is demonstrated by a numerical simulation study

Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process

The remaining useful life (RUL) prediction of lubricating oil is essential for the preventive maintenance of machines, while the prediction accuracy has been severely limited by sparse and truncated data. Stochastic process modeling can provide a potential solution. However, two main challenges are encountered: 1) the nonlinear parameter estimation is prone to overfitting due to sparse data and 2) the lack of failure samples for threshold determination with truncated data. In the article, a novel RUL prediction model is developed based on the Wiener process and the oil degradation mechanism. Primarily, data augmentation is adopted to enhance data quantity for reliable nonlinear parameter estimation. Furthermore, the run-to-failure prediction based on the probability density function is performed to obtain the threshold with the truncated data. With the well-trained model, the RUL prediction is accomplished by updating the parameters and the thresholds with monitoring data. Furthermore, the prediction accuracy is validated with the oil data collected from both simulations and bench tests.

Remaining Useful Life Prediction of Lubricating Oil With Small Samples

The application of industrial robots in manufacturing industries has received considerable concerns due to the high flexibility, multifunctionality, and cost-efficiency. It is well known that the robot positioning accuracy is susceptible to the load and motion of robots owing to the insufficient stiffness of robots. Therefore, the machining accuracy improvement has been a research focus in the robotic manufacturing industries in the last decade. To overcome the measurement difficulty of the joint torque and position as well as the complex dynamic coupling between rotors and links, two forward dynamics algorithms for the robot deflection estimation are proposed in this paper. The robot kinematics and dynamics algorithms considering the dynamic coupling between rotors and links are developed based on Lie theory. The forward dynamics equations of robots are solved via the proposed algorithms: the implicit numerical integration algorithm and numerical iterative estimation algorithm. When only the motor position is available, the implicit numerical integration algorithm is employed to solve the forward dynamics equations to estimate the joint torque and position. At the same time, when both the motor position and torque are available, the forward dynamics equations can be reorganized as algebraic equations and solved by the numerical iterative estimation algorithm. Simulations of a 6-DOF serial robot are performed to verify the accuracy of the proposed algorithms.

Deflection estimation of industrial robots with flexible joints

Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

https://journals.sagepub.com/doi/pdf/10.1177/20552076231173569

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.

Yaguo Lei

Papers

Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process

Remaining Useful Life Prediction of Lubricating Oil With Small Samples

Deflection estimation of industrial robots with flexible joints

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Machine remaining useful life prediction considering unit-to-unit variability