다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

https://escholarship.org/content/qt5ns8r3fs/qt5ns8r3fs.pdf?t=pihyfp

Remaining useful life estimation in prognostics using deep convolution neural networks

An overview of time-based and condition-based maintenance in industrial application

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Machine components and systems, such as gears, bearings, pipes, cutting tools and turbines, may experience various types of faults, such as breakage, crack, pitting, wear, corrosion. If not being properly monitored and treated, such faults can propagate and lead to machinery performance degradation, malfunction, or even severe component/system failure. It is significant to reliably detect machinery defects, evaluate their severity, predict the fault propagation trends, and schedule optimized maintenance and inspection activities to prevent unexpected failures. Advances in these areas will support ensuring equipment and production reliability, safety, quality and productivity. Machine fault diagnostics and prognostics tools have wide potential applications in various industries particularly manufacturing, aerospace, transportation, oil and gas energy. The aim of this special issue on ‘‘Machine Fault Diagnostics and Prognostics’’ is to present new methods and tools for equipment condition monitoring, fault diagnostics, and equipment prognostics aiming at predicting fault propagation and remaining useful life. As a result of this Call for Papers, over 30 full-length papers were submitted, which passed all rounds of anonymous review processes typical for the Chinese Journal of Mechanical Engineering. Ten papers were accepted and included in this special issue. Induction motor fault diagnosis methods werereported in several papers. For example, Wang et al. proposed an online detection method based on Min-Norm algorithm and least square estimation, to improve precision and calculation speed of fault diagnosis on broken rotorbars in induction motors. Li et al. developed an intelligent harmonic synthesis technique for conducting incipient air-gap eccentricity fault detection in induction motors, and the effectiveness of the proposed technique is examined experimentally. Advanced signal processing techniques were proposed to recognize representative features for fault detection in machinery systems. Li et al. suggested an adaptive morphological update lifting wavelet method for rolling element bearing fault detection. Jiang et al. investigated vibration based machine fault diagnosis based on unsupervised multiscale representation learning, in order to capture rich and complementary fault pattern information at different scales. Li et al. studied gripper cylinders that provide braced force for a Tunnel Boring Machine; an online empirical mode decomposition based method was proposed for online fault diagnosis. Wang et al. suggested an adaptive change detection method using incremental sliding-window, to recognize structural changes from an operational process for long-term machinery health condition monitoring. Deep learning was investigated by researchers for machine fault diagnosis. Shao et al. developed a deep learning approach based on deep belief networks, in order to learn features from frequency distribution of vibration signals for the purpose of health condition evaluation of induction motors. Wang et al. investigated a motor fault diagnosis method based on short-time Fourier transform and convolutional neural network; its effectiveness was validated using experimental data. Fault propagation assessment and prediction methods were also reported in this special issue. Liang et al. & Zhigang Tian ztian@ualberta.ca

/pdf/special-issue-on-machine-fault-diagnostics-and-prognostics-2bwd9g1ivo.pdf

Special Issue on Machine Fault Diagnostics and Prognostics

Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms

New PM2.5 forecasting system based on combined neural network and an improved multi-objective optimization algorithm: Taking the economic belt surrounding the Bohai Sea as an example

Large manufacturing firms usually sign a specific demand response contract with the utility provider, and agreed to curtail the load upon request. Both the curtailment time and the reduction level are uncertain prior to the contingency call. In response, manufacturing firms could turn off the machines or slow down the production under the revenue losses. In this paper, we propose a new demand response scheme in which the manufacturer adopts wind and solar energy as the power reserve to achieve the load reduction compliance. We leverage the well-known newsboy inventory model to determine the optimal capacity of renewable generating units with the goal to maximize the manufacturer's electricity savings.

Managing demand response for manufacturing enterprises via renewable energy integration

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

/pdf/a-novel-wind-speed-interval-prediction-system-based-on-2lycev9z.pdf

Zhigang Tian

Papers

Special Issue on Machine Fault Diagnostics and Prognostics

Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms

New PM2.5 forecasting system based on combined neural network and an improved multi-objective optimization algorithm: Taking the economic belt surrounding the Bohai Sea as an example

Managing demand response for manufacturing enterprises via renewable energy integration

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization