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The healthy operation conditions of wind turbines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target WT. First, the data of the target and source WTs are preprocessed to construct a training set. Second, to improve the TrAdaBoost algorithm for the OCC task, a novel weighting method based on an autoencoder (AE)—an unsupervised one-class classifier—is designed to assign weights for samples in the source and target domain dynamically and then the CM model of the target WT is established. Finally, its effectiveness is verified by using the monitoring data to detect the generator fault of the target WT. The comparison with the nontransfer method shows that the proposed method can significantly reduce the number of false alarms and can issue early warnings. [ABSTRACT FROM AUTHOR]

Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented. [ABSTRACT FROM AUTHOR]

System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM. [ABSTRACT FROM AUTHOR]

This letter proposes a new submodule capacitor condition monitoring method for modular multilevel converters (MMCs) based on the dc-side start-up procedure. During this process, the MMC can be simplified into an RC charging circuit, and the submodule capacitance can be estimated by investigating its relationship with the phase current and submodule voltage. Several practical issues related to the implementation, advantages, and limitations of the proposed method are presented as well. In order to validate the method, a series of experiments with different submodule capacitances are conducted based on a three-phase MMC platform. Experimental results confirm the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Condition monitoring (CM) has been playing an important role in the operation and maintenance of industrial equipment. However, the changeable environments where some equipment, such as wind turbines (WTs) are installed, have put a negative effect on CM. To deal with this problem, this article proposes a CM method of WTs based on assembled multidimensional membership functions (MFs) using a fuzzy inference system. First, multidimensional MFs, which are formed by fusing environment factor into conventional MFs, are proposed to reduce the negative effect from changeable environments. Second, the input data are properly divided into segments and the segments are classified into four types to distinguish the effects of different data on CM. Different weights are assigned to each segment and the corresponding membership of each segment is calculated separately. Then, these memberships are assembled for fuzzy inference. Finally, based on the assembled multidimensional MFs, a new CM architecture is established. Four groups of experiments were carried out to evaluate the proposed method with the data collected from a wind farm in northern China. The experiments results show that the proposed method can not only detect anomalies at an early stage but also effectively decrease the false alarms and missing detections. [ABSTRACT FROM AUTHOR]

The key function of rotating machine condition monitoring (CM) is to detect structural changes during machine operations. This paper presents a novel statistical time-frequency analysis method for this purpose. In particular, frequency spectrum is extracted from the machine condition signals based on periodogram estimation. Undirected weighted graph is then constructed from the resulting periodograms, where the so-called median graph is introduced and adopted to describe the normal machine status. Statistical analysis is performed to investigate newly observed data with respect to the median graph for change decision making. The proposed method has been applied to three different engineering applications to evaluate its effectiveness: load CM; early bearing failure detection; and speed CM. The results were compared with some benchmark methods reported in the literature, where significant improvements of the proposed method were demonstrated, indicating its good potentials in engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, a health assessment method for electronic components subject to condition monitoring (CM) is presented, which can be used to estimate the conditional reliability characteristics given the current operational age and the corresponding degradation state. The degradation process is characterized by a continuous-time Markov chain, which is incorporated into a Cox’s proportional hazard model to describe the hazard rate of the time to failure. The two main challenges encountered in the health assessment of electronic components subject to CM and hard failure, i.e., the large number of degradation states and the general deteriorating transition mechanism, can be properly addressed by the proposed method. To illustrate the effectiveness of the proposed method, a case study of power metal-oxide-semiconductor field-effect transistors is performed as a representative example of a general electronic application. The method for the model parameter estimation is developed and applied using the degradation and failure data obtained from an accelerated testing experiment, based on which the conditional reliability function, the mean residual life, and other health characteristics can be explicitly calculated using the proposed discretization technique. The excellent accuracy of the health assessment demonstrates the effectiveness and advantages of the proposed method applied to a real electronic component subject to CM and hard failure. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies. [ABSTRACT FROM PUBLISHER]

Rotating machinery assets are a fundamental necessity in the mining industry. Furthermore, the proper maintenance and ability to monitoring these key assets is important to maximizing the operations. This paper will discuss the merits and advancements in vibration monitor systems and the direct impact on supervisory control and data acquisition and predictive maintenance models. [ABSTRACT FROM PUBLISHER]

Condition monitoring (CM) has been recognized as a more effective failure prevention paradigm than the time-based counterpart. CM can be performed via an array of sensors providing multiple, real-time equipment degradation information with broad coverage. However, loss of sensor readings due to sensor abnormalities and/or malfunction of connectors has long been a hurdle to reliable fault diagnosis and prognosis in multichannel CM systems. The problem becomes more challenging when the sensor channels are not synchronized because of different sampling rates used and/or time-varying operational schemes. This paper provides a nonparametric sensor recovery technique and a semi-parametric alternative to enhance the robustness of multichannel CM systems. Based on historical data, models for all the sensor signals are constructed using functional principal component analysis (FPCA), and functional regression (FR) models are developed for those correlated signals. These models with parameters updated in online implementation can be used to recover the lost sensor signals. A case study of aircraft engines is used to demonstrate the capability of the proposed approaches. In addition to recovering asynchronous sensor signals, the proposed approaches are also compared with the Elman neural network as a popular alternative in recovering synchronous sensor signals. [ABSTRACT FROM AUTHOR]