Search results

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy inference system.

In process plants, equipment condition is ascertained using condition‐monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition‐monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition‐monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition‐monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge.

The proposed approach has been applied to a large electric motor (> 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition‐monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition‐monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as “very good”, “good”, “marginal” and “sick”. Degree of certainty has helped in taking a managerial decision to avoid “over‐maintenance” and to ensure reliability. Large volumes of condition‐monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals.

Condition‐monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability.

The paper gives an opportunity to evaluate quantitative condition‐monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle “Monitor‐analyse‐plan‐repair‐restore‐operate” is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

Reciprocating compressors offer an efficient method of compressing almost any gas composition in a wide range of pressures and have numerous applications. Condition monitoring of critical rotating machinery is widely accepted by operators of centrifugal compressors. However, condition monitoring of reciprocating machinery has not received the same degree of acceptance. An earlier study (Townsend et al., 2016) was conducted on temperature monitoring. The purpose of this paper is to examine the impact of continuous pressure monitoring on electric-driven compressors.

This research analyzes the impact of continuous pressure monitoring on a fleet of 14 compressors transporting CO₂ for enhanced oil recovery. The reliability and efficiency data on 14 reciprocating compressors over a three-year period were analyzed for failures detectable by the condition monitoring technology. The engineering economic analysis is presented to determine the impact this technology will have on the productivity of the compressors.

The study considers utilizing condition monitoring technology to analyze the pressure of the swept volume of the compressor cylinders. The results of the study indicate that continuous pressure monitoring technology has a strong impact on the productivity of the compressor fleet. The internal rate of return not only exceeds the operators hurdle rate, but the payback period is also dramatic. Pressure monitoring was found to be economically better than temperature monitoring.

The study reveals the economic benefits of implementing condition monitoring in the form of continuous pressure monitoring on reciprocating compressors.

This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.

Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.

The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.

An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.

The common data quality problems encountered in CBM decision analyses are investigated with a view to suggesting methods to resolve these problems. In particular, the approaches for handling missing data in the decision analysis are reviewed.

This paper proposes a data structure for managing the asset‐related maintenance data that support CBM decision analysis. It also presents a procedure for data‐driven CBM optimization comprising the steps of data preparation, model construction and validation, decision‐making, and sensitivity analysis.

Analysis of condition monitoring data using the proportional hazards modeling (PHM) approach has been proved to be successful in optimizing CBM decisions relating to motor transmission equipment, power transformers and manufacturing processes. However, on many occasions, asset managers still make sub‐optimal decisions because of data quality problems. Thus, mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. This paper examines the significant issues of data management in CBM decision analysis. In particular, the requirements of data captured from two common condition monitoring techniques – namely vibration monitoring and oil analysis – are discussed.

This paper offers advice to asset managers on ways to avoid capturing poor data and the procedure for manipulating imperfect data, so that they can assess equipment conditions and predict failures more accurately. This way, the useful life of physical assets can be extended and the related maintenance costs minimized. It also proposes a research agenda on CBM optimization and associated data management issues.

Maintenance represents a significant proportion of the overall operating costs in the mining industry. Despite the large cost of maintenance, management has only given passing attention to the optimization of the maintenance process. The focus has remained on the optimization of mine planning and operations where all the low hanging fruit was picked years ago. Recent initiatives in the field of mobile equipment maintenance have been in the area of remote condition monitoring. In order for an advanced maintenance technology to succeed it must have a strong philosophical basis and the supporting hardware and software infrastructure. A high bandwidth radio network, reliable interfaces, and a real‐time maintenance management system will enable remote condition monitoring systems. Explores reliability centered maintenance, remote condition monitoring, and the use of production and maintenance data for real‐time interactive maintenance management.

The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and Kenya, respectively).

Empirical observations and theoretical interpretations on maintenance practices under APM are delineated. A comparative cross-sectional survey study is conducted through an online questionnaire with 151 respondents (101 Kenya, 50 Belgium). Descriptive statistics and inferential statistics like independent t-test and phi coefficient were used for analyzing the data.

In both countries, reduction of maintenance and operational budget, return on assets, asset ageing and compliance aspects were established as critical factors influencing the implementation of asset maintenance and performance management (AMPM). A significant difference in staff competence in managing vibration, ultrasound and others like predictive algorithms was found to exist between the firms of the two countries. The majority of firms across the divide utilize manual and computer-based tools to integrate and analyse various maintenance data sets, while standardization and maintenance knowledge loss were found to adversely affect maintenance data management.

The study findings are based on the limited number of returned responses of the survey questionnaire and focused on only two countries representing developed and developing economies. This study not only provides practitioners with the practical guidelines for benchmarking, but also induces the need to improve the asset maintenance strategies and data application practices for asset performance management.

The paper provides insights to researchers and practitioners in the articulation of imperative effective maintenance strategies, benchmarking and challenges in their implementation, considering the different operational context.

The paper contributes to theory and practice within the field of AMPM where no empirical research comparing developed and developing countries exist.

The purpose of this paper is to develop a comprehensive approach to efficiently integrate maintenance and operation by combining process and condition monitoring data with performance measures.

Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. The data analysis resulting nonlinear scaling functions can also be used to handle performance measures used for management. The generalised norms provide limits for an advanced statistical process control.

The data‐driven analysis methodology demonstrates that management‐oriented indicators can be presented in the same scale as intelligent condition and stress indices. Control, condition monitoring, maintenance and performance monitoring are represented as interactive feedback loops.

Performance analysis can be based on real‐time information by using various stress, condition and health indices as inputs. Similar approaches can be used for outputs: quality indices, harmonised indices, key performance indicators, process capability indices and overall equipment effectiveness. Since consistent linguistic explanations based on nonlinear scaling are available for all these indices, the analysis can be further deepened with LE modelling. Efficient monitoring with intelligent indices provides a good basis for control and condition‐based maintenance and performance monitoring.

The paper extends the nonlinear scaling methodology and linguistic equations to intelligent performance measures. The methodology provides a consistent way to also represent all information with linguistic terms.

Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed along with their respective failures and repairs over a nine‐year period. Detailed data cleaning procedures were applied to prepare data for modeling. In addition, definitions of failure and suspension were clarified depending on equipment condition at replacement. Using the proportional hazards model approach, the key condition variables relating to failures were found from among the 19 elements monitored, plus sediment and viscosity. Those key variables were then incorporated into a decision model that provided an unambiguous and optimal recommendation on whether to continue operating a wheel motor or to remove it for overhaul on the basis of data obtained from an oil sample. Wheel motor failure implied extensive planetary gear or sun gear damage necessitating the replacement of one or more major internal components in a general overhaul. The decision model, when triggered by incoming data, provided both a recommendation based on an optimal decision policy as well as an estimate of the unit’s remaining useful life. By optimizing the times of repair as a function both of age and condition data a 20‐30 percent potential savings in overhaul costs over existing practice was identified.

The developments of complex systems have increased the demand for condition monitoring techniques so as to maximize operational availability and safety while decreasing the costs. Signal analysis is one of the methods used to develop condition monitoring in order to extract important information contained in the sensory signals, which can be used for health assessment. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multi-dimensional and obscured by noise. The paper aims to discuss this issue.

This paper presents a method for trends extraction from multi-dimensional sensory data, which are then used for machinery health monitoring and maintenance needs. The proposed method is based on extracting successive features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition (EMD) algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time.

The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and a second data set acquired form NASA repository.

The method showed that it is able to extract interesting signal trends which can be used for health monitoring and remaining useful life prediction.

The purpose of this paper is to analyze the effects of condition-based maintenance based on unscheduled maintenance delays that were caused by ATA chapter 21 (air conditioning). The goal is to show the introduction of condition monitoring in aircraft systems.

The research was done using the Airbus In-Service database to analyze the delay causes, delay length and to check if they are easy to detect via condition monitoring or not. These results were then combined with delay costs.

Analysis shows that about 80 percent of the maintenance actions that cause departure delays can be prevented when additional sensors are introduced. With already existing sensors it is possible to avoid about 20 percent of the delay causing maintenance actions.

The research is limited on the data of the Airbus in-service database and on ATA chapter 21 (air conditioning).

The research shows that delays can be prevented by using existing sensors in the air conditioning system for condition monitoring. More delays can be prevented by installing new sensors.

The research focuses on the effect of the air conditioning system of an aircraft on the delay effects and the impact of condition monitoring on delays.