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 its lifecycle. 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. The approach 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.

Preventing catastrophic failures is the most important task of prognostics and health management approaches in industry where Remaining Useful Life (RUL) prediction plays a significant role to schedule required preventive actions. Regarding recent advances and trends in data analysis and in Big Data environment, industries with such foreseeing approach are able to maintain their fleet of assets more efficiently with higher assurance. To address this requirement, several physics-based and data-driven methods have been developed to predict the remaining useful life of various engineering systems. In current paper, we present a simple, yet accurate stochastic method for data-driven RUL prediction of complex engineering system. The approach is constructed based on selecting the most significant parameters from raw data by using the improved distance evaluation method as feature selection algorithms. Subsequently, the health value of units is assessed by logistic regression and the assessment output is used in a Monte Carlo simulation to estimate the remaining useful life of the desired system. During Monte Carlo iterations, several features are extracted to help filtering less accurate estimations and improve the overall prediction accuracy. The proposed algorithm is validated in two ways. First of all, the accuracy of RUL prediction is measured by applying the method to 2008 PHM data challenge gas-turbine dataset. Subsequently, gradual changes in RUL prediction of a particular test unit are measured to verify the behavior of the algorithm upon availability of additional historical data.

Power generation performance is a fundamental metric that all wind farm operators use to determine whether expected power throughput is actually being met. IEC 61400-12-1 has been drafted as an exhaustive power performance measurement scheme for wind turbines. The primary weakness of such a standard is the required level of depth of the associated performance tests, which is more than sufficient for operators to use to run daily wind farm activities. In addition, since this IEC test is not really meant for frequent evaluation, it also fails to capture any loss in power generation performance over time. This paper addresses the aforementioned weaknesses of the IEC standard by the application of data-driven approach to model a wind turbine's power curve. A set of measurements during a known good condition is utilized to setup a baseline model. Regular power curve measurements are then compared while taking into account the multi-regime dynamics of the turbine. The approach was implemented using NI LabVIEW's Watchdog Agent (R) Toolkit and was successfully validated using actual SCADA data collected from an on-shore wind turbine.

As diesel engines play a critical role in various applications, the ability for a health monitoring system to perform early fault diagnosis is of vital importance for the reliable functioning of a diesel engine throughout its service. In regard to the criticality of diesel engine fault diagnosis, an abundance of research efforts have been conducted by adopting injection-based, vibration-based, or instantaneous speed-based methods. The study presented in this paper discusses an integrated approach of diagnosing combustion faults and valve leakage, by combining vibration signal with cylinder pressure and revolution speed signals. Signal processing methods, including time frequency domain analysis such as wavelet decomposition are applied to extract features from data collected under different health conditions. The dimension of the feature set is further reduced based on discriminant analysis. Classification techniques are subsequently evaluated as fault diagnosis tools. The proposed approach is validated on a small-scale diesel engine test bed, and is to be applied in a real world health monitoring system.

Much research has been conducted. in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system. Published by Elsevier Ltd.

Nowadays, energy has become a key issue in all sectors of industry. Analytical tools in the prognostics and health management (PHM) area are needed to transform energy and related data into actionable information. The IMS Center has developed corresponding solutions for energy generation, storage, and usage applications. The Watchdog Agent® toolbox techniques are applied in a systematic way in each application to address the development of advanced predictive tools for near-continuous uptime of energy generating assets; mobility readiness and safety for next-generation electric vehicles via the Smart Battery Agent; and the application of low-cost, nonintrusive predictive solutions using equipment energy consumption. In this paper, methodologies and case studies in the three categories are presented.

To achieve worry-free production and prevent unexpected downtime, predictive maintenance has become a prevailing strategy, supported by prognostics and health management techniques. In this paper, a systematic data-driven approach for intelligent maintenance is discussed, as well as an embedded system architecture for fault detection and prediction. Two case studies are presented, including performance prediction for wire-twisting machine based on temperature data, and an embedded solution for electric valve actuator monitoring.

ABSTRACTThe 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.

Rolling-element bearings are critical components in locomotive traction motors. A reliable online bearing fault-diagnostic technique is critically needed to prevent motor systems performance degradation and malfunction. Motor bearing failure induces vibration, resulting in the modulation of the stator current. Compared with conventional monitoring techniques such as vibration monitoring or temperature monitoring, stator current-based monitoring offers significant economic benefits and implementation advantages. In this paper, a novel approach to locomotive traction motor current signature analysis based on wavelet packet decomposition (WPD) of stator current is presented. The effectiveness and practicability of the proposed method is verified by locomotive running tests.

In the industrial energy monitoring domain, several platforms are offered to acquire real-time power data and monitor energy consumption at tool, component, machine and system levels. The motivations of employing such platforms are majorly cost-effective purposes or environmental issues. This paper proposes and applies a power-based approach with non-intrusive sensing technique to evaluating machine health status. Data of effective power and reactive power are acquired synchronously and segmented based on machine cycles. Exhaustive feature extraction and reduction is conducted to reduce the dimension of the datasets. Two health assessment models are then used to model baseline behavior from the training set and measure the distance between training and testing set to provide health status information of the testing set with respective distance metrics. The models are applied in two case studies on two machines in the same manufacturing line.