Implementation Of Condition-based Maintenance Research Articles

An integrated linguistic Pythagorean fuzzy decision-making approach for risk analysis of offshore wind turbine

The evaluation of risk factors within the context of condition-based maintenance (CBM) for offshore wind turbines has been a challenging issue. In this paper, a novel risk analysis method that combines the fuzzy weighted with zero inconsistency (FWZIC) technique and the DEMATEL method under the linguistic Pythagorean fuzzy environment is proposed. Firstly, 75 risk factors linked to offshore wind turbines are identified by leveraging analysis and prior literature, which are modeled using linguistic Pythagorean fuzzy sets (LPFSs) to facilitate the complexity of expert opinions in the face of uncertainty. Then, the hybrid expert weight calculation method and the FWZIC-based criteria weight calculation method are introduced to determine the weights of the experts and the criteria while considering uncertainty. Next, the extended DEMATEL method under the linguistic Pythagorean fuzzy environment is adopted to determine the significance and influence of the pinpointed risk factors, and an illustrative cause–effect diagram is crafted to dissect the interconnections between these factors. Through this intricate analysis, the cause factors and effect factors specific to offshore wind turbines are identified, and the results obtained from the proposed method are validated through sensitivity analysis and comparative analysis. The results of this study could provide valuable recommendations and references for the implementation of CBM in offshore wind turbines.

Condition-based maintenance implementation: a literature review

Industrial companies are increasingly dependent on the availability and performance of their equipment to remain competitive. This circumstance demands accurate and timely maintenance actions in alignment with the organizational objectives. Condition-Based Maintenance (CBM) is a strategy that considers information about the equipment condition to recommend appropriate maintenance actions. The main purpose of CBM is to prevent functional failures or a significant performance decrease of the monitored equipment. CBM relies on a wide range of resources and techniques required to detect deviations from the normal operating conditions, diagnose incipient failures or predict the future condition of an asset. To obtain meaningful information for maintenance decision making, relevant data must be collected and properly analyzed. Recent advances in Big Data analytics and Internet of Things (IoT) enable real-time decision making based on abundant data acquired from several different sources. However, each appliance must be designed according to the equipment configuration and considering the nature of specific failure modes. CBM implementation is a complex matter, regardless of the equipment characteristics. Therefore, to ensure cost-effectiveness, it must be addressed in a systematic and organized manner, considering the technical and financial issues involved. This paper presents a literature review on approaches to support CBM implementation. Published studies and standards that provide guidelines to implement CBM are analyzed and compared. For each existing approach, the steps recommended to implement CBM are listed and the main gaps are identified. Based on the literature, factors that can affect the effective implementation of CBM are also highlighted and discussed.

A model for financial viability of implementation of condition based maintenance for induction motors

PurposeWhen induction motors are considered, there is no specific cost model for net savings per year due to condition-based maintenance (CBM) covering various parameters such as downtime, energy, quality, etc. The purpose of this paper is to develop a cost model for the financial viability of the implementation of CBM for induction motors.Design/methodology/approachA literature review has been carried out to identify the existing failure modes of motor, available condition monitoring techniques, the usefulness of CBM and different maintenance models available. Then, a cost model considering all parameters has been proposed.FindingsA cost model has been proposed for the maintenance of induction motors. Method for the economic evaluation of the model has also been suggested in the paper. The application of the model has been illustrated through a case study of a steel plant, which suggests that investment in the condition monitoring of induction motors increases the net profit of the organization.Research limitations/implicationsThe proposed model is specifically designed for induction motors. All the motors under consideration are assumed to be of the same specifications, and fault in any motor is supposed to have the same effect on quality, cost, criticality, etc., of the operation and end product.Practical implicationsThis paper will help the maintenance manager in decision making when maintenance action has to be carried out for a given motor under CBM for the better utilization of the equipment and resources. This paper also shows how to compute ROI on CBM investment.Originality/valueThe paper provides a cost model for the economic evaluation of implementing CBM for induction motors which will be useful to researchers and maintenance managers in effective decision making and maintenance planning. The methodology and the cost models are the original contribution of the authors.

Prognostic Model Development with Missing Labels

Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.

A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach

Integrated consideration of production planning and maintenance processes is a real world assumption. Specifically, by improving the monitoring equipment such as various sensors or product-embedded information devices in recent years, joint assessment of these processes is inevitable for enhancing the level of the system optimization. By means of this equipment, managers can benefit from a condition-based maintenance (CBM) for monitoring and managing their system. The chief aim of the paper is to develop a stochastic maintenance problem based on CBM activities engaged with a complex applied production problem called flexible job shop scheduling problem (FJSP). This integrated problem considers two maintenance scenarios in terms of corrective maintenance (CM) and preventive maintenance (PM). The activation of scenario is done by monitoring the degradation condition of the system and comparing the associated value by predetermined PM and CM levels. Moreover, to make it more realistic, the developed problem allows breakdown of the system between inspection intervals. The event time and duration of performing the maintenance activities are also considered stochastic. The developed methodology copes with high stochastic complexity of proposed problem through simulation-based optimization (SBO) approach, which works based on harmony search optimization algorithm. The developed items of SBO are discussed on different generated test problems and assessed through statistical methods and new visualization approach. Numerical example shows that the proposed method is practical for proposed integrated CBM and FJSP problem. It also offers a basis for any implementation of CBM on production problems.

A Damage Evaluation Model of Turbine Blade for Gas Turbine

Current maintenance, having a great impact on the safety, reliability and economics of a gas turbine, becomes the major obstacle for the application of gas turbines in energy field. An effective solution is to process condition based maintenance (CBM) thoroughly for gas turbines. Maintenance of high temperature blade, accounting for the most of the maintenance costs and time, is the crucial section of gas turbine maintenance. The suggested life of high temperature blade by original equipment manufacturer (OEM) is based on several certain operating conditions, which is used for time based maintenance (TBM). Thus, for the requirement of gas turbine CBM, a damage evaluation model is demanded to estimate the life consumption online. A physics-based model is built, consisting of thermodynamic performance simulation model, stress estimation model, thermal estimation model, and interactive damage analysis model. Unmeasured parameters are simulated by the thermodynamic performance simulation model, as the input of the stress estimation model and the thermal estimation model. Due to the ability to analyze online data, this model can be used to calculate online damage and support CBM decision. Then the stress and temperature distribution of blades will become as the input of the creep damage analysis model and the fatigue damage analysis model. The interactive damage of blades will be evaluated based on the creep and fatigue analysis results. To validate this physics-based model, it is used to calculate the lifes of high temperature blade under several certain operating conditions. And the results are compared to the suggestion value of OEM. An application case is designed to evaluate the application effect of this model. The result shows that the relative error of this model is less than 10.4% in selected cases. And it can cut overhaul costs and increase the availability of gas turbines significantly. Finally, a simple application of this model is proposed to show its functions. The physical-based damage evaluation model proposed in this paper is found to be a useful tool to tracing the online life consumption of a high temperature blade, to support the implementation of CBM for gas turbines, and to guarantee the reliability of gas turbines with lowest maintenance costs.

APPLICATION OF MONITORING, DIAGNOSIS, AND PROGNOSIS IN THERMAL PERFORMANCE ANALYSIS FOR NUCLEAR POWER PLANTS

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

A Model of Weight Absolute Grey Correlation Degree for the Transformer Bushing State Assessment

Accurate assessment for the operational status of the transformer bushing is not only a prerequisite for the implementation of condition-based maintenance, but also to ensure the normal operation of the transformer and the whole power equipment conditions. In the paper, a model of weight absolute grey correlation degree was used to assess the transformer bushing state. Firstly, determined the assessment indicators and handled them. Secondly, the weights, correlation coefficients and each association were calculated. Thirdly, to compare the largest association of assessment indicators in grades as the transformer bushing final run state. Finally, give the state assessment results, according to the remark set. The result shows the model works well.

Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

A distributed Architecture to implement a Prognostic Function for Complex Systems

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs provide information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its subsystems, functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to increase the calculation speed. It is carried out by generic agents.

Condition-based maintenance in punching/blanking of sheet metal

This paper provides a review and proposal for condition-based maintenance (CBM) in blanking of sheet metal. To date, little research on this topic can be found in literature, which is probably due to the complex nature of the process. Previous statistical, artificial intelligence (AI) and model-based approaches are analysed. Special attention is given to inherent assumptions and other sources of inaccuracy. In addition, it is demonstrated how the signature of the force–displacement relation changes significantly with increasing tool wear in a typical configuration of sheet steel blanking. An analysis follows as to how such behaviour could be used in CBM. A practical implementation of CBM in sheet metal blanking is proposed based on a hybrid solution.

Singularity detection in machinery health monitoring using Lipschitz exponent function

Machinery health monitoring is a key step in the implementation of Condition-based Maintenance in industry. In this procedure, a quantitative description of machine health condition is necessary for maintenance decision-making. In this paper, we applied singularity analysis with wavelet for data processing and a new concept, Lipschitz exponent function, was proposed based on wavelet transform. A kurtosis based health index was defined, which can be used for maintenance decision-making. The proposed method was validated with two sets of gearbox vibration data in comparison with three other indexes. The results show that kurtosis based health index demonstrates excellent performance.

Building evaluation methodology for setting maintenance priorities in hospital buildings

Condition‐Based Maintenance (CBM) is a common solution for the maintenance of large complex facilities under tight economic conditions. Effective implementation of CBM requires the development of performance indicators for building components and systems. The objectives of the proposed methodology are: (1) to monitor the condition of building components, based on systematic performance scales; (2) to develop a parameter that ensures clear detection of building elements that are in failing condition; and (3) to provide a Key Performance Indicator, based on a unified scale, for the comparison of the performance of different buildings. Two rating scales are used: one for the individual components (Pn ) and a second for the entire building – the Building Performance Indicator (BPI). The first scale combines criteria regarding the physical state, performance, fitness for use, and preventive maintenance of various building components. The second scale, the BPI, is a 100‐point scale that covers 10 of the building's main systems. The proposed methodology was implemented in 17 public health care facilities in Israel. The survey results show that the overall state of the facilities is that of deterioration (BPI = 68.9). One building system, the communication and low voltage system, was consistently found to be in poor condition (Pn = 39.4), four systems (exterior envelope, interior finishing, water and waste‐water, and HVAC) were in deteriorating condition (60d ≤ Pn < 70), and five were in satisfactory or higher condition. Validity of the methodology was tested by means of a survey executed by 14 trained surveyors on the same facility. The variance of the results was relatively slight.

Condition monitoring of stator windings in induction motors. II. Experimental investigation of voltage mismatch detectors

For pt.I see ibid., vol.38, no.5, p.1447-53 (2002). Condition-based maintenance (CBM) of industrial equipment is generally recognized as being the most cost-effective means for improving equipment availability. However, a prerequisite to successful implementation of CBM is a reliable detector of failing components. One such detector, termed the effective negative-sequence impedance, had previously been identified as an indicator of an induction motor stator winding degradation. However, a limitation of this detector is that it may not change in a predictable manner for extremely low levels of deterioration. Presented in this paper is an experimental investigation of a technique for online detection of induction motor stator winding degradation that addresses this difficulty. It begins with a brief description of the detectors, followed by a detailed description of the experimental setup, the experiments conducted, and results.