An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

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

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

The ability to detect and diagnose incipient gear and bearing degradation can offer substantial improvements in reliability and availability of the wind turbine asset. Considering the motivation for improved reliability of the wind turbine drive train, numerous research efforts have been conducted using a vast array of vibration-based algorithms. Despite these efforts, the techniques are often evaluated on smaller-scale test-beds, and existing studies do not provide a detailed comparison between the various vibration-based condition monitoring algorithms. This study evaluates a multitude of methods, including frequency domain and cepstrum analysis, time synchronous averaging narrowband and residual methods, bearing envelope analysis and spectral kurtosis-based methods. A full-scale baseline wind turbine drive train and a drive train with several gear and bearing failures are tested at the National Renewable Energy Laboratory (NREL) dynamometer test cell during the NREL Gear Reliability Collaborative Round Robin study. A tabular set of results is presented to highlight the ability of each algorithm to accurately detect the bearing and gear wheel component health. The results highlight that the cepstrum and the narrowband phase modulation signal were effective methods for diagnosing gear tooth problems, whereas bearing envelope analysis could confidently detect most of the bearing-related failures. Copyright © 2013 John Wiley & Sons, Ltd.

A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at different time horizon. Therefore, the final RUL of a specific component can be estimated and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

/pdf/remaining-useful-life-estimation-using-time-trajectory-4s3fevd3ok.pdf

Remaining useful life estimation using time trajectory tracking and support vector machines

Method and apparatus for assessing health of fracturing fluid pump assemblies and other wellsite equipment. For example, predicted data indicative of a first operational parameter of a pump assembly is generated utilizing: a model relating the first operational parameter to each of a plurality of second operational parameters of the pump assembly; and real-time data indicative of each of the second operational parameters. Health of the pump assembly is then assessed based on: the predicted data indicative of the first operational parameter; and real-time data indicative of the first operational parameter.

Pump Assembly Health Assessment

As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over itslifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. Theapproach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.

/pdf/an-integrated-framework-of-drivetrain-degradation-assessment-2cfq7a4s5z.pdf

Wenyu Zhao

Papers

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

Remaining useful life estimation using time trajectory tracking and support vector machines

Pump Assembly Health Assessment

An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines