Equipment Condition Assessment Research Articles

Identification of the tensile damage of degraded carbon steel and ferritic alloy-steel by acoustic emission with in situ microscopic investigations

The paper presents results obtained from the destructive laboratory investigation conducted on materials from pressure vessels after long-term operation in the refinery industry. Tested materials contained structural defects, which arose from improper heat treatment during steel plate manufacturing. Detailed metallographic and chemical composition tests and static tensile tests were conducted. Next, complex tensile tests were conducted with simultaneous acoustic emission (AE) monitoring while observing microstructural changes by light microscopy. From the laboratory tests, the correlations between the AE signal parameters and material microstructural damage during the tensile tests were developed. The results will be used as a basis of new algorithms for the structural condition assessment of in-service pressure equipment.

Integrating Laplacian Eigenmaps Feature Space Conversion into Deep Neural Network for Equipment Condition Assessment

Reliable equipment condition assessment technique is playing an increasingly important role in modern industry. This paper presents a novel method by integrating Laplacian Eigenmaps (LE) that transforms data features from original high-dimensional space to projected low-dimensional space to extract the more representative features into deep neural network (DNN) for equipment health assessment, in which the bearing run-to-failure data were investigated for validation studies. Through a series of comparison experiments with the original features, two other popular space transformation methods principal component analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence algorithms hidden Markov model (HMM) and back-propagation neural network (BPNN), the proposed method in this paper was proved more effective for equipment condition evaluation.

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.

An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs

The polymeric materials used for insulation and sheath in instrumentation and control (I&C) cables of nuclear power plants (NPPs) are subjected to degradation due to various stressors. The prediction of long-term aging and lifetime of cables is generally determined based on accelerated life testing (ALT) experiments which are not only expensive but also time consuming. Application of artificial neural networks (ANNs) in the field of transient diagnosis and condition assessment of electrical and other equipment has been a promising technique; however the use of ANN for reliability prediction of I&C cables has not yet been studied. This paper presents an integrated approach to predict the lifetime and reliability of I&C cables by ANN from the accelerated aging data. In order to validate the proposed methodology for use in probabilistic safety assessment (PSA) of NPP to account for the cable failures, ALT data on a typical cross-linked polyethylene (XLPE) insulated I&C cable has been referred from the literature. The time dependent reliability was predicted by considering the various failure rates. Study demonstrates that by an appropriate training algorithm with suitable network architecture, it is possible to predict the reliability of I&C cables by ANN with the minimal accelerated life testing.

The Selection and Placement Method of Materialized Views on Big Data Platform of Equipment Condition Assessment

With the formation of electric big data environment, more and more big data analyses emerge. In the complicated data analysis on equipment condition assessment, there exist many join operations, which are time-consuming. In order to save time, the approach of materialized view is usually used. It places part of common and critical join results on external storage and avoids the frequent join operation. In the paper we propose the methods of selecting and placing materialized views to reduce the query time of electric transmission and transformation equipment, and make the profits of service providers maximal. In selection method we design a computation way for the value of non-leaf node based on MVPP structure chart. In placement method we use relevance weights to place the selected materialized views, which help reduce the network transmission time. Our experiments show that the proposed selection and placement methods have a high throughput and good optimization ability of query time for electric transmission and transformation equipment.

Self-adaptive separation of multiple partial discharge sources based on optimized feature extraction of cumulative energy function

Partial discharge (PD) measurement is an effective tool for insulation condition assessment of high-voltage power equipment. The occurrence of multiple PD sources causes great difficulty on pattern recognition and failure risk assessment. This paper presents a self-adaptive PD separation algorithm based on optimized feature extraction of cumulative energy (CE) function. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. By using an oblique line to cross the CE curves, width features are extracted from the intersection points between them. Through the mathematical morphology gradient (MMG) operation, sharpness features are extracted to characterize the rise steepness of CE. It is found that the separation capability of width and sharpness are dependent on the pre-selected oblique line and the structure element length (SEL) in MMG, respectively. In order to obtain satisfactory PD separation results for various experimental conditions, a density-function based parameter is proposed to evaluate the separation capability, and the oblique line and SEL are optimized with the goal of maximizing the evaluation parameter. A clustering algorithm is adopted to discover different clusters in feature space and separate PD signals. The separation algorithm is examined with mixed PD current pulses and ultra-high-frequency (UHF) signals acquired from experiments in laboratory and on-site equipment. The results indicate that the self-adaptive separation method is immune to the change of experimental conditions, and is effective for separating mixed PD signals.

Investigation on the Applicability of the Time Domain Analysis of Discharges in Gases for the Defect Identification at AC Voltage

The partial discharge diagnosis is an established instrument for the condition assessment of high voltage insulations and equipment. Under AC voltage stress the phase resolved pattern is of great significance in order to become aware of the type of fault. As a result of the inapplicability for DC voltage stress, approaches for alternative interpretation techniques such as the time domain analysis of partial discharges were identified in recent investigations. In these different types of fault are taken into account as well as different insulating media. The purpose of this paper is to investigate whether an analysis of the pulse shape is also applicable for the defect identification under AC voltage stress. By focussing on gaseous insulating media, contact noise and surface discharges are emulated in ambient air, whereas corona discharges are emulated in ambient air and oxygen. A method for analysing discharges, occurring in the negative and the positive half-wave of the test voltage, is proposed and discussed.

Cache Management of Big Data in Equipment Condition Assessment

Big data platform for equipment condition assessment is built for comprehensive analysis. The platform has various application demands. According to its response time, its application can be divided into offline, interactive and real-time types. For real-time application, its data processing efficiency is important. In general, data cache is one of the most efficient ways to improve query time. However, big data caching is different from the traditional data caching. In the paper we propose a distributed cache management framework of big data for equipment condition assessment. It consists of three parts: cache structure, cache replacement algorithm and cache placement algorithm. Cache structure is the basis of the latter two algorithms. Based on the framework and algorithms, we make full use of the characteristics of just accessing some valuable data during a period of time, and put relevant data on the neighborhood nodes, which largely reduce network transmission cost. We also validate the performance of our proposed approaches through extensive experiments. It demonstrates that the proposed cache replacement algorithm and cache management framework has higher hit rate or lower query time than LRU algorithm and round-robin algorithm.

基于云模型的智能变电站二次设备状态评估

变电站二次设备状态评估是实现状态检修的基础，是保证一次设备安全运行的关键工作，而二次设备故障机理复杂，存在隐性故障，其状态评估所需指标更为复杂。提出基于云模型的智能变电站二次设备状态评估方法，建立基于云模型状态评估方法流程，划分评估智能变电站二次设备状态等级，采用层析分析法变权重方法进行处理。结果表明采用云模型的评价方法能更准确的评估智能变电站二次设备运行状态。为智能变电站二次设备状态检修工作提供理论依据。 Substation secondary equipment condition assessment is the basis to realize condition mainten-ance, and the key ensuring the safe operation of the equipment. However, the mechanism of sec-ondary equipment fault is complex, which may have recessive fault, and the index required is more complex. This paper proposes a new method for smart substation secondary equipment condition assessment based on Cloud Model, establishes evaluation flow process, divides the condition level, and uses the analytic hierarchy process and varied weight process. The results show that this method can evaluate the smart substation secondary equipment condition more accurately, and provide theoretical basis for the maintenance of it.

Dynamic Condition Assessment of Electrical Equipments Based on Markov Prediction

A novel method for electrical equipments condition assessment is proposed based on Markov prediction. Firstly, the condition representation parameters are induced according to the general volt-ampere characteristics, which can be identified by least square method. Secondly, the number of samples falling into different intervals is counted and the state transition probability matrix is calculated. Last, the distribution of parameters in future detection periods is predicted and the equipments condition is assessed by the ratio of the length of current operation time and the estimated life. Take a single phase two-winding transformer as an example, Monte Carlo simulation is used to generate the leakage inductance data and the electrical data is derived by PSCAD. The equipment condition assessment results verify the effectiveness and feasibility of this proposed method.

基于变阈值的船舶动力装置状态监测与评估研究

在分析单一阈值监测法在船舶动力装置等大型机械设备状态监测领域存在不足的基础上，提出了基于RBF网络的变阈值监测模型，并以该模型为基础，设计了船舶动力装置状态评估流程，最后以X型舰艇主柴油机为例，对监测模型和评估流程进行了验证。On basis of analyzing the single threshold monitoring method’s shortcomings in ship power plant and other large machinery condition monitoring areas, this paper puts forward the variable thre-shold monitoring model based on RBF network, and on the basis of the model, designs the ship power equipment condition assessment process. Finally, we take X ship main diesel engine as an example to test the monitoring model and the evaluate process.

Incorporating Equipment Condition Assessment in Risk Monitors for Advanced Small Modular Reactors

Advanced small modular reactors (aSMRs) can complement the current fleet of large light-water reactors in the USA for baseload and peak demand power production and process heat applications (e.g., water desalination, shale oil extraction, hydrogen production). The day-to-day costs of aSMRs are expected to be dominated by operations and maintenance (OM however, the effect of diverse operating missions and unit modularity on OM this approach neglects the time-varying condition of equipment that is relied on during standard and non-standard configurations. Equipment condition monitoring techniques can be usedmore » to estimate the component POF. Incorporating this unit-specific estimate of POF in the risk monitor can provide a more accurate estimate of risk in different operating and maintenance configurations. This enhanced risk assessment will be especially important for aSMRs that have advanced component designs, which don’t have an available operating history to draw from, and often use passive design features, which present challenges to PRA. This paper presents the requirements and technical gaps for developing a framework to integrate unit-specific estimates of POF into risk monitors, resulting in enhanced risk monitors that support optimized operation and maintenance of aSMRs.« less

Regression analysis to evaluate the reliability of insulation diagnostic methods

Abstract The condition assessment of high voltage equipment requires reliable diagnostic measurements. The reliability of an insulation diagnostic method can be examined by accelerated laboratory tests besides the analysis of failure statistics. Laboratory tests are used to find appropriate diagnostic method to insulation or insulating material. The aim of these laboratory tests is to determine the correlation between the ageing and the measured parameters however, in many cases, the direct interaction between main deterioration processes and the examined phenomenon is not still clearly recognized. In this paper, parallel measurements of laboratory aged low voltage cables are evaluated by regression analysis.

The Condition Based Maintenance Evaluation Model on On-post Vacuum Circuit Breaker

The safe operation of power supply equipments is closely related to the security of electric network. The planned maintenance of existing power equipments o cannot meet the needs of development of power system. To solve the problems in maintenance for vacuum circuit breaker, this paper build the equipment condition and risk assessment index system and bring out the outdoor on-post vacuum circuit breaker condition based maintenance evaluation model which based on Rough Set and Support Vector Machine according to the real condition. To prove the high accuracy of this method, a research which about the data of 100 Box-type substation in the distributing network of one power supply company is conducted in this paper.

Condition Assessment of Road Equipment - State-of-the-Art

Road equipment is used for increasing safety and comfort in traffic. There are 5 main groups of equipment: road lighting, fences and barriers, vertical signs, horizontal signs and traffic signals. ...

The economics of aging infrastructure

Eventually, every system will age to a sustainable point at which repair and replacement costs driven by advanced equipment age and deterioration from long service will remain constant thereafter. The good news is that equipment data, condition assessment, prioritization, and management focus can significantly reduce costs, extend lifetimes, and improve reliability. The bad news is that even when optimized to the extent possible, in the long run, utilities will need more money in order to maintain their systems at the lowest life-cycle cost. Many regulatory agencies and customers will push back and insist that spending related to aging infrastructure be reasonably justified, but limiting a utility's ability to manage its aging equipment will eventually result in lower levels of reliability and higher rates in the future

SYCLOPS—a qualitative debris classification system developed for RAF early failure detection centres

Wear debris analysis of material originating from aircraft lubrication systems is one of the main stays of the Royal Air Force equipment health-monitoring programme. Although this is an effective tool in the assessment of equipment condition, it is labour intensive and relies heavily upon the experience of the operator. For the past 10 years there has been an ongoing Ministry of Defence sponsored research programme to develop WDA procedures for application within RAF Early Failure Detection Centres. This paper summarises this work to date and describes how this has resulted in the successful development of the SYCLOPS wear debris characterisation package.

Applications of dielectric spectroscopy in time and frequency domain for HV power equipment

Dielectric spectroscopy in the time or frequency domain offers new opportunities for an off-line, on-site insulation condition assessment of HV electric power equipment and its predictive maintenance.

Model-aided diagnosis-a new method for online condition assessment of high voltage circuit breakers

In recent years, online condition assessment of high voltage equipment is increasingly asked for by power utilities, however, most systems on the market today are merely monitoring solutions with hardly any diagnostic capabilities. This report reviews the basic principles for condition diagnosis from the field of artificial intelligence and introduces a novel strategy, named model-aided diagnosis (MAiD). It was initially developed for condition assessment of high voltage circuit breakers but is also suitable for other technical devices. Theoretical considerations as well as practical results show that MAiD is a useful strategy for condition assessment. In addition, with its low hardware requirements, it allows online condition diagnosis of high voltage circuit breakers locally by a low-cost monitoring device.

Engineered Approach to Effective Maintenance Management

ABSTRACTThe Assessment of Equipment Condition (AEC) Program at the Naval Ship Systems Engineering Station has been providing shore based support to assess the condition of shipboard equipment. This paper examines some of the limitations of the existing process. The paper offers an engineered approach to provide condition assessment on board ship and the shore side support necessary to maximize the effectiveness of the total ship maintenance process. The recommended approach uses existing technology and current hardware and software applications. Included in the approach is the use of microcomputer machinery work stations with expert system software, and a central Condition Maintenance Information System with the capability to provide work packages based on condition assessment and to trend the effectiveness of maintenance decisions. The paper identifies a phased implementation with a limited pilot program prior to fleet wide involvement.