Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing

Operational expenditure optimisation utilising condition monitoring for offshore wind parks

There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable.There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable

Predictive maintenance decision support system for enhanced energy efficiency of ship machinery

A decision support system (DSS) is an application that analyses data and presents results to users. DSS rapidly shift through huge amount of available data and thus allowing for faster analysis of condition monitoring data early detection of faults and improved allocation of resources. DSS can also predict and plan for future ship operators’ needs in order to optimize ship machinery operations. Such a system can provide substantial benefits to the maritime industry in terms of energy efficiency as the operation of the vessel can be optimised towards this end. As part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) EU FP7 project, this paper presents a novel DSS solution which interrogates data from dynamic condition monitoring and compares them with historic data to present decision support information onboard a ship. To provide for Condition Based inspection and criticality based maintenance for ship machinery, data is acquired and stored for analysis through the DSS. Moreover surveys involving off-line and real time on-line measurement approaches are combined to provide a more complete monitoring method. The result is a reliable user friendly graphical interface (GUI) developed in Java language that can be employed onboard any vessel and can provide relevant and on-time information. The proposed actions from the DSS target energy efficient operation and reduction of fuel consumption and ship emissions. Moreover, a major factor taken into account through the prediction mechanism of the DSS is to assist in better spare parts scheduling and prioritizing ship inspection, maintenance and repairs towards enhanced and efficient ship operations

Adaptive management of technical condition of power transformers

Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment

A Novel Scalable Decision Tree Implementation on SoC Based FPGAs

Machine learning algorithms are rapidly growing in predictive maintenance and condition monitoring systems for valuable assets. Decision tree classification (DTC) is one of popular methods in condition monitoring systems based on vibration analysis. Due to big amount of data coming out from vibration sensors, the processing should be done on edge close to the sensors. DTC can reach high accuracy but at the same time it is computationally intensive and edge processors are not able to run it so fast. There are some FPGA implementation that work fine for small datasets but have issues when there is a real big dataset that needs deep trees. In this paper we introduce our new method of Decision Tree (DT) implementation on SoC based FPGAs. We have shown that using a combination of FPGA and processor, the DT can be implemented much faster and more scalable for trees with depth up to 50. We have used Vivado HLS to implement our DTs and connected them to the processor of SoC via AXI interfaces. We have shown that our implementation gains up to 2.27x speed up comparing with only software implementation.acceptedVersio

Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.This research received no external fundin

Load and risk based maintenance management of wind turbines

<p> Wind power has proven to be an important source of renewable energy in the modern electric power systems. Low profit margins due to falling electricity prices and high maintenance costs, over the past few years, have led to a focus on research in the area of maintenance management of wind turbines. The main aim of maintenance management is to find the optimal balance between Preventive Maintenance (PM) and Corrective Maintenance (CM), such that the overall life cycle cost of the asset is minimized. This thesis proposes a maintenance management framework called Self Evolving Maintenance Scheduler (SEMS), which provides guidelines for improving reliability and optimizing maintenance of wind turbines, by focusing on critical components. <p> The thesis introduces an Artificial Intelligence (AI) based condition monitoring method, which uses Artificial Neural Network (ANN) models together with Supervisory Control And Data Acquisition (SCADA) data for the early detection of failures in wind turbine components. The procedure for creating robust and reliable ANN models for condition monitoring applications is presented. The ANN based Condition Monitoring System (CMS) procedure focuses on issues like the selection of configuration of ANN models, the filtering of SCADA data for the selection of correct data set for ANN model training, and an approach to overcome the issue of randomness in the training of ANN models. Furthermore, an anomaly detection approach, which ensures an accuracy of 99% in the anomaly detection process is presented. The ANN based condition monitoring method is validated through case studies using real data from wind turbines of different types and ratings. The results from the case studies indicate that the ANN based CMS method can detect a failure in the wind turbine gearbox components as early as three months before the replacement of the damaged component is required. An early information about an impending failure can then be utilized for optimizing the maintenance schedule in order to avoid expensive unscheduled corrective maintenance. <p> The final part of the thesis presents a mathematical optimization model, called the Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC), for optimal maintenance decision making. The PMSPIC model provides an Age Based Preventive Maintenance (ABPM) schedule, which gives an initial estimate of the number of replacements, and an optimal ABPM schedule for the critical components during the life of the wind turbine, based on the failure rate models created using the historical failure times. Modifications in the PMSPIC model are presented, which enable an update of the maintenance decisions following an indication of deterioration from the CMS, providing a Condition Based Preventive Maintenance (CBPM) schedule. A hypothetical but realistic case study utilizing the Proportional Hazards Model (PHM) and output from the ANN based CMS method, is presented. The results from the case study demonstrate the possibility of updating the maintenance decisions in continuous time considering the changing conditions of the damaged components. Unlike the previously published mathematical models for maintenance optimization, the PMSPIC based scheduler provides an optimal decision considering the effect of an early replacement of the damaged component on the entire lives of all the critical components in the wind turbine system

Bayesian Filtering Methods For Dynamic System Monitoring and Control

Real-time system monitoring and control represent two of the most important issues that characterize modern industries in critical areas of civilian and military interest, including the power grid, energy, healthcare, aerospace, and infrastructure. During the past decade, there has been a rapid development of robust dynamic system monitoring and control methods for fault diagnosis and failure prognosis. Among various monitoring and control policies, condition-based maintenance (CBM) has been studied by many researchers due to its ability to enable a large amount of monitoring data for real-time diagnostics and prognostics. A considerable amount of literature has been published on the subject, providing a large volume of dynamic system control methods. Previously published studies are limited by assumptions that can generally be distinguished into three main categories: i) predefined system failure thresholds, ii) simplified latent dynamics, and iii) unrealistic parametric forms that describe the evolution of system dynamics through time. This thesis provides an array of solution approaches that overcome the aforementioned assumptions in a smart and effective way by introducing novel quantitative frameworks for real-time monitoring, control, and decision-making for dynamic systems. The proposed frameworks are categorized into two main phases of a comprehensive framework. The first phase contains two original Bayesian filtering methods for condition monitoring and control of systems with either linear or non-linear degradation dynamics. The former is designed only for systems with linear latent and observable dynamics and utilizes Kalman filtering for state-parameter inference. It considers a failure process that is purely stochastic and is based on logistic regression. This process is directly affected by the latent system dynamics, therefore avoiding the need for a priori failure thresholds. The latter takes into consideration multiple levels of system dynamics that evolve either linearly or non-linearly. A hybrid particle filter is developed for state-parameter inference, while an Extreme Learning Machine artificial neural network is utilized to relate sensor observations to latent system dynamics. Both frameworks are tested and validated on synthetic and real-world time-series datasets. The second phase of this thesis introduces an original method for optimal control and decision-making that employs Bayesian filtering-based deep reinforcement learning with fully stochastic environments. Sets of deep reinforcement learning agents were trained to develop control policies. Bayesian filtering methods from the first phase were utilized to provide environment states that use the estimates from latent system dynamics. This method is used in two different applications for maintenance cost minimization and estimating the remaining useful life of a system under condition monitoring. Results obtained from applying the framework on simulated and real-world time-series data suggest that the proposed Bayesian filtering-based deep reinforcement learning algorithm can be trained even with limited data, which can be useful for real-time control and decision making for many dynamic systems

Evaluation of strategic building maintenance and refurbishment budgeting method Schroeder

The method Schroeder is accepted amongst real estate professionals in Switzerland as a near standard for condition monitoring, budgeting of maintenance and refurbishment, and strategic decision support in point of building portfolios. It is based on the devaluation curves of 12 or more building elements. Main results are the actual and the prognosticated future building condition in percentage of its reinstatement value, the residual useful service life of building elements, and the calculation of future maintenance and refurbishment costs. 25 years after its first publication, this paper analyses the assumptions made, compares the method to other methods in this field, and validates the method in several steps, based on scientific or empirical evidence. Furthermore, a desktop simulation of a well-documented portfolio was performed and compared, the answers from a questionnaire amongst users are provided, and the partially controversial conclusions are discussed

Patch detection for pavement assessment

Pavement management systems rely on comprehensive up-to-date road condition data to provide effective decision support for short, medium and long term maintenance scheduling. However, the cost per mile of the existing condition data collection methods allows only for periodical surveys. This leads to long gaps between inspections and a focus on major roads over rural ones. Therefore, pavement condition monitoring systems that provide inexpensive frequent updates on the road condition are necessary. Such systems would require robust and automatic defect detection methods using low-cost sensors. In this paper, one such method is proposed for detecting road patches from video data acquired by the car's parking camera. A patch is initially detected based on its visual characteristics, which are: 1) it consists of a closed contour and 2) its texture is the same with the surrounding intact pavement. The patch is then passed to a kernel tracker in order to trace it in subsequent video frames. This way redetection is avoided and each patch is reported only once. The method was implemented in a C# prototype and tested with video data consisting of approximately 4000 frames collected from roads in Cambridge, UK. The results show that the suggested method has 84% precision and 96% recall.This material is based upon the work supported by the National Science Foundation (NSF Grant #1031329). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.This is the author accepted version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.autcon.2015.03.01