Maintenance Decision Analysis Research Articles

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.

Strategic Reliability Centered Maintenance Decision Analysis Model Using Fuzzy and Analytic Hierarchy Process for Refinery Valves

Modern industries, particularly oil and gas, necessitate robust maintenance management and optimization for increased profits, plant availability, and maintenance cost reduction. The maintenance strategy is therefore crucial, especially under global economic pressures on equipment reliability. This study examines the present maintenance management practices for refinery valves, critical equipment whose failure can have severe consequences. Many existing models for selecting critical equipment in maintenance management face limitations due to inadequate decision analysis and exclusion of key criteria. This research proposes a strategy model, combining artificial intelligence and multi-criteria decision-making techniques like Fuzzy Logic and Analytical Hierarchy Process, to fill this gap and manage refinery valves' maintenance. Proven by case studies and interviews, the model improves safety, reliability, operational availability and enables operators to monitor their decision consequences. The model can convince asset managers to invest in maintenance initiatives.

Combining RAMS with EEP for performance-based maintenance: a review

PurposeThe study aims to perform an extensive literature review in the area of the maintenance decision analysis (MDA), especially in power generation systems. In the basis of this review, the paper proposes a new model for the MDA which involves a combination of reliability, availability, maintainability and safety (RAMS) performance with energy efficiency performance (EEP).Design/methodology/approachStarting from the opportunity in Sustainable Development Scenario (SDS) by improving the energy efficiency (EE) and using renewable energy for the power generation system, also concerning to the major challenge of maintenance optimization in order to implement maintenance strategy, the maintenance decision-making and energetic efficiency management system (EEMS) have been reviewed. In the context of power generation system's performance, the measurement is also analyzed and identified. Then, the extensive literature review has been performed to compare between RAMS and EEP. And finally, the limitation and gap, where EEP is not yet a complementary consideration during MDA being identified and a new model for the performance-based MDA is proposed.FindingsThe new model proposed for the performance-based MDA is able to be used to conduct maintenance decision by utilizing the combination of RAMS and EEP depending on the type of decision required.Practical implicationsThere is an opportunity for a maintenance organization of power generation plant to apply this new model proposed for the MDA in order to optimize the maintenance scope and schedule.Originality/valueThe result of work in this paper forms the basis for combining RAMS with EEP as performance-based MDA tools in the context of maintenance of the power generation system.

Condition Maintenance Decision of Wind Turbine Gearbox Based on Stochastic Differential Equation

Maintenance decision analysis is necessary to ensure the safe and stable operation of wind turbine equipment. To address gearboxes with a high failure rate in wind turbines, this paper establishes a new stochastic differential equation model of gearbox state transition to maximize the utilization of gearboxes. This model divides the state of the gearbox into two parts: internal degradation and external random interference. Weibull distribution and polynomial approximation were used to construct the internal degradation model of the gearbox. The external random interference is simulated by Brownian motion. On the basis of the analysis of monitoring data, the parameters of the gearbox state model were solved using the Newton–Raphson iterative method and entropy method. The state change of the gearbox was simulated in MATLAB, and the residual value between the predicted state and the real state was calculated. Compared with the state transformation model constructed by the traditional ordinary differential equation and the gamma distribution, the Weibull polynomial approximation stochastic model can better reflect the state of the device. With reliability set as the decision goal, the maintenance time of the gearbox is predicted, and the validity of the model is verified through case analysis.

Replacement and Maintenance Decision Analysis for Hydraulic Machinery Facilities at Reservoirs under Imperfect Maintenance

After the long-term operation of reservoir facilities, they will become nonoperational due to the material deterioration and the performance degradation. One of crucial decisions is to determine the maintenance or replacement of the facilities in a cost-effective manner. Conventional replacement models seldom consider the maintenance effect. The facilities after maintenance are generally not as good as new, but are relatively restored. The target of this study is to establish a replacement decision model of the reservoir facilities under imperfect maintenance. By combining the theories of reliability analysis, imperfect maintenance, and engineering economics, the best timing of replacement that achieves cost-effectiveness is analyzed and proposed. Lastly, based on the design of experiments (DOE) and simulation, the regression curve chart for the economical replacement decision is established. Once the failure rate, the age of recovery after maintenance, and the ratio of maintenance cost to replacement cost are estimated based on historical data, the cost-effective replacement time of hydraulic machinery facilities will be efficiently determined.

A Condition-Based Maintenance Policy (CBM) of Repairable Multi-Component Deteriorating Systems Based on Quality Information

The maintenance decision analysis of equipment has always been one of the hot spots of concern for researchers. A maintenance policy for parallel production systems is investigated in this study, with the parallel components having equal cost parameters. The rejection rate indicates the degradation degree of the machine, and the rejection rate is assumed to be obtained by detection. The maintenance strategy proposed in this paper involves two threshold values. By optimizing with genetic algorithm, the threshold values [Formula: see text] and [Formula: see text] corresponding to the minimum expected total cost can be found. Finally, the result of sensitivity analysis is shown that the cost parameter has a great influence on the threshold values.

Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis - A Book Review

The objective of this work is the book “Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis”, 978-3-319-17968-1, from Springer Series “ International Series in Operations Research & Management Science ” review.

Relationship between Delay Time and Gamma Process Models

The delay-time concept and gamma process model have been widely applied in CBM settings. This paper reveals a close relationship between these two models. That is, given one of them, the other can be approximately determined. This nature is very useful in reliability modeling and maintenance decision analysis based on the delay time model or gamma process model because some analysis is easier for one of them than for the other. The method to make mutual conversions between these two models is developed and illustrated by three examples. The potential applications and possible extensions of the method are also discussed.

A probabilistic description scheme for rotating machinery health evaluation

Condition-based maintenance has become more popular in recent years because of its advantages in terms of minimizing downtime, extending lifetime, and reducing cost. This kind of maintenance strategy is based on condition monitoring of machinery in operation. Ccondition monitoring is a key step in maintenance decision analysis. Numerous non-stationary signal processing methods have been developed to reveal fault characteristics in rotating machinery. In this study, an adaptive signal analysis method called empirical mode decomposition is employed for gearbox vibration signal preprocessing. Considering a modulation phenomenon that appeared in a faulty gear, the Hilbert Transform is applied to obtain an envelope signature, which usually contains abundant fault-related signatures. Being different from other failure classification problems, this paper is concerned with determining the probability of normal condition based on current observations describing the condition of a gearbox. Moreover, according to Bayes rule, this problem can be translated to estimate the conditional probability of current observations given normal gearbox condition using a Hidden Markov Model. From this point, a novel probabilistic health description index called Average Probability Index is proposed for gearbox health evaluation. For automatic detection, a semi-dynamic threshold is presented to detect an early fault in a gear. At last, validation and comparative studies are performed using two sets of gearbox lifetime accelerated testing vibration data. The results show the advantages of the proposed method for gearbox condition monitoring.

Gearbox vibration monitoring using extended Kalman filters and hypothesis tests

The efficiency of many maintenance programs is heavily dependent on the detection accuracy of the condition monitoring system. Condition indicators that are sensitive to environmental or operational variables of no interest will inevitably reflect irrelevant fluctuations and thus mislead the subsequent analysis. In consideration of this phenomenon, a fully automatic and robust vibration monitoring system for gearboxes is proposed in this study. The primary objective here is on how to exclude the effects of variable load conditions. The proposed technique features a number of appealing advantages, which include extended Kalman filter-based time-varying autoregressive modeling, automatic autoregressive model order selection with the aid of a non-paired two-sample Satterthwaite's t′-test, a highly effective and robust condition indicator (the means of one-sample Kolmogorov–Smirnov goodness-of-fit test), and an automatic alert generating mechanism for incipient gear faults with the aid of a Wilcoxon rank-sum test. Two sets of entire lifetime gearbox vibration monitoring data with distinct variable load conditions were used for experimental validation. The proposed condition indicator was compared with other well-known and/or recently proposed condition indicators. The results demonstrate excellent performance of the proposed technique in four aspects: the effectiveness of identifying the optimum model order, a minimum number of false alerts, constant behavior under variable load conditions, and to some extent an early alert for incipient gear faults. Furthermore, the proposed condition indicator can be directly employed by condition-based maintenance programs as a condition covariate for operational maintenance decision analysis. It provides a quantitative and more efficient means for exchanging condition information with maintenance programs in comparison with the widely used non-parametric time–frequency techniques such as wavelets, which rely on visual inspection.

Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic. Part II: Experiment and application

Optimal maintenance decision analysis is heavily dependent on the accuracy of condition indicators. A condition indicator that is subject to such varying operating conditions as load is unable to provide precise condition information of the monitored object for making optimal operational maintenance decisions even if the maintenance program is established within a rigorous theoretical framework. For this reason, the performance of condition monitoring techniques applied to rotating machinery under varying load conditions has been a long-term concern and has attracted intensive research interest. Part I of this study proposed a novel technique based on adaptive autoregressive modeling and hypothesis tests. The method is able to automatically search for the optimal time-series model order and establish a compromised autoregressive model fitting based on the healthy gear motion residual signals under varying load conditions. The condition of the monitored gearbox is numerically represented by a modified Kolmogorov–Smirnov test statistic. Part II of this study is devoted to applications of the proposed technique to entire lifetime condition detection of three gearboxes with distinct physical specifications, distinct load conditions, and distinct failure modes. A comprehensive and thorough comparative study is conducted between the proposed technique and several counterparts. The detection technique is further enhanced by a proposed method to automatically identify and generate fault alerts with the aid of the Wilcoxon rank-sum test and thus requires no supervision from maintenance personnel. Experimental analysis demonstrated that the proposed technique applied to automatic identification and generation of fault alerts also features two highly desirable properties, i.e. few false alerts and early alert for incipient faults. Furthermore, it is found that the proposed technique is able to identify two types of abnormalities, i.e. strong ghost components abruptly appearing in and disturbing the nominal operating process and the breakage of gear teeth, whereas conventional condition indicators are usually not capable of detecting the presence of the former.

Aircraft maintenance jacking problem via optimization

We address the problem of assigning forces to jacking positions in order to weaken stress at points where an aircraft maintenance operation has to be performed. We introduce a mixed-integer linear programming model and report encouraging computational experiments on historical data. Our methodology is currently under the process of industrial implementation at Airbus, where it will be used as a maintenance decision-analysis tool.