Corrective Maintenance Actions Research Articles

Imperfect and worse than old maintenances for a gamma degradation process

AbstractThis article considers a condition‐based maintenance for a system subject to deterioration. The deterioration is modeled by a non‐homogeneous gamma process, more precisely the gamma process and the preventive maintenance are imperfect or worse than old. The corrective maintenance actions are as good as new. The maintenance efficiency or non‐efficiency parameters as well as the deterioration parameters are considered to be unknown. The monitoring data under consideration give indirect information on the maintenance parameters. Therefore, an expected maximum algorithm is applied for parameter estimation.

Dynamic opportunistic maintenance grouping in a lot streaming based job-shop scheduling problem

This paper aims to model maintenance planning with a dynamic opportunistic approach for a job-shop production system. One issue in such a system is the positive or negative economic dependency. That is grouping maintenance activities may decrease or increase system costs. Furthermore, many maintenance models consider the planning of maintenance only based on a long-term horizon. While short-term and real circumstances such as system characteristics and constraints, workload, number of available maintenance teams, and variable maintenance cost and time are almost ignored. To address these issues, a rolling-horizon approach based on a long-term maintenance plan is proposed so that subsequent scheduling of maintenance and production activities are performed as events unfold through the time. Hence, we have developed a mixed-integer nonlinear mathematical model to simultaneously make decisions on maintenance selection, maintenance grouping, lot sizing and production scheduling. The objective function includes the costs of preventive and corrective maintenance activities as well as various production costs such as production and setup, tardiness penalty, and safety stock penalty. A self-adaptive Cuckoo Optimization Algorithm has been used to solve the proposed model. Numerical experiments were conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the optimization algorithm.

Integration of operational lockout/tagout in a joint production and maintenance policy of a smart production system

Accidents during maintenance operations are frequently occurring and sometimes even fatal. The lack of energy locking is one of the main factors of accidents during maintenance. The Lockout/Tagout (LOTO) process aims to ensure the safety of workers by providing a control of equipment energies before and after preventive or corrective maintenance activities. However, LOTO is often seen as an unnecessary delay that increases production costs. The objective of this work is to minimize production and inventory costs of finished products, while guaranteeing the safety of workers during maintenance activities by the implementation of operational LOTO actions, which allows for production continuity during the securing of energy sources. This work studies the problem of production and maintenance planning of a smart production system that deteriorates according to its age affecting its reliability and availability. The system under study consists of a production unit processing a single type of parts and exposed to random failures and repairs. The decision variables for this control problem are the machine age dependent production and preventive maintenance rates of the machine. The proposed model is based on the stochastic optimal control theory and the optimality conditions are obtained by the dynamic programming approach. Such conditions are of the Hamilton-Jacobi-Bellman (HJB) type and have been solved by numerical methods for concrete industrial case applications. The developed model is validated using a numerical example and a sensitivity analysis is conducted to study the behavior of the system related to the variations of some parameters. A comparative study shows that the proposed operational LOTO strategy performs better than a traditional LOTO strategy in optimizing maintenance operations and reducing incurred costs. This study proposes new managerial policies in terms of joint management of production, maintenance and safety.

Predict the priority of end-users’ maintenance requests and the required technical staff through LSTM and Bi-LSTM recurrent neural networks

PurposeThis paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).Design/methodology/approachThis study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.FindingsThe study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.Research limitations/implicationsThis work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.Practical implicationsThe data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.Social implicationsThe improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.Originality/valueThis study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage

The detection of leakages in Water Distribution Networks (WDNs) is usually challenging and identifying their locations may take a long time. Current water leak detection methods such as model-based and measurement-based approaches face significant limitations that impact response times, resource requirements, accuracy, and location identification. This paper presents a method for determining locations in the WDNs that are vulnerable to leakage by combining six leakage-conditioning factors using logistic regression and vulnerability analysis. The proposed model considered three fixed physical factors (pipe length per junction, number of fittings per length, and pipe friction factor) and three varying operational aspects (drop in pressure, decrease in flow, and variations in chlorine levels). The model performance was validated using 13 district metered areas (DMAs) of the Sharjah Electricity and Water Authority (SEWA) WDN using ArcGIS. Each of the six conditioning factors was assigned a weight that reflects its contribution to leakage in the WDNs based on the Analytic Hierarchy Process (AHP) method. The highest weight was set to 0.25 for both pressure and flow, while 0.2 and 0.14 were set for the chlorine and number of fittings per length, respectively. The minimum weight was set to 0.08 for both length per junction and friction factor. When the model runs, it produces vulnerability to leakage maps, which indicate the DMAs’ vulnerability classes ranging from very high to very low. Real-world data and different scenarios were used to validate the method, and the areas vulnerable to leakage were successfully identified based on fixed physical and varying operational factors. This vulnerability map will provide a comprehensive understanding of the risks facing a system and help stakeholders develop and implement strategies to mitigate the leakage. Therefore, water utility companies can employ this method for corrective maintenance activities and daily operations. The proposed approach can offer a valuable tool for reducing water production costs and increasing the efficiency of WDN.

Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems

The aim of this article was to solve a multi-objective maintenance optimization problem by minimizing both unavailability and cost through the use of an optimal maintenance strategy. The problem took into account three different system designs upon which the objective functions are dependent, and the time to start preventive maintenance (PM) was used as a decision variable. This variable was optimized for all system components using a discrete maintenance model that allows for the specification of several discrete values of the decision variable in advance to find the optimal one. The optimization problem was solved using innovative computing methodology and newly updated software in MATLAB, which was used to quantify the unavailability of a complex system represented through a directed acyclic graph. A cost model was also developed to compute the cost of different maintenance configurations, and the optimal configuration was found. The results for a selected real system (a real fluid injection system adopted from references) showed that unavailability was less sensitive to variations in maintenance configurations, while cost variations were more noticeable in relation to different maintenance configurations. Applying PM, the increasing value of the decision variable increased cost because it led to more frequent corrective maintenance (CM) actions, and recovery times due to CM were more expensive than recovery times due to PM.

Global sensitivity analysis of a generator-absorber heat exchange (GAX) system’s thermal performance with a hybrid energy source: An approach using artificial intelligence models

• Temperature, heat and flow measurements of an experimental GAX cycle. • Evaluation of artificial intelligence (AI) techniques for multivariate modeling. • Artificial neural networks represent the GAX system with the best accuracy. • AI model coupled to the PAWN method for a global sensitivity analysis of GAX cycle. • COP is affected the most by the temperature measured at the generator inlet. Generator-absorber heat exchange (GAX) systems represent a promising alternative to substitute environmentally harmful refrigeration devices based on conventional vapor compression, as long as a proper analysis of thermal performance and the complex interactions of heat transfer that occur into GAX cycle is taken in consideration. In this research, a cooling process based on a GAX system that uses ammonia-water working fluid and a hybrid source (natural gas-solar) is studied to analyze the variables that affect the system’s thermal performance. The work’s novelty is the hybridization between artificial intelligence (AI) modeling and the global sensitivity analysis (GSA) developed with the PAWN method. Experimental data was obtained from a system with a cooling capacity of 10.5 kW (3 Ton), designed to work at heat source temperatures of 200 °C. The measured variables were the temperatures at generator, heat at evaporator, and working fluid volumetric flow. Three AI techniques (artificial neural networks, genetic programming, and support vector machines) were evaluated for modeling the thermodynamic cycle. Results obtained from the PAWN method applied to the artificial neural network, since it was the best AI model, indicates that the operational parameters with a greater impact in the system’s performance are the inlet temperature at the generator (30.7 %) and the heat measured at the evaporator for NH 3 (27.4 %), for the first output COP N H 3 . For the second output COP H 2 O , the inlet temperature at the generator (32.5 %) and the and heat measured at the evaporator for H 2 O (26.7 %), have a greater impact for such output. The proposed IA-GSA methodology contributes to the development of operational decision-making related to instrumentation, operation performance, and corrective and/or preventive maintenance actions of GAX systems. The developed thermal performance model has potential for implementation in embedded systems (smart sensors) as a critical element in control and optimization strategies to improve the performance of these cycles.

General Independent Competing Risks for Maintenance Analysis

Abstract Repairable systems are submitted to corrective maintenance and condition-based preventive maintenance actions. Condition-based preventive maintenance occurs at times which are determined according to the results of inspections and degradation or operation controls. The generalization of the models suggested makes it possible to integrate the dependence between corrective and preventive maintenances. In order to take into account this dependency and the possibility of imperfect maintenances, generalized competing risks models have been presented in Doyen and Gaudoin (2006). In this study, we revise the general case in which the potential times to next corrective and preventive maintenance are independent conditionally to the past of the maintenance process. We address the identifiability issue and we find a result similar to that of Zhou, Lu, Shi and Cheng (2018) for usual competing risks. We propose realistic models with exponential risks and derive their likelihood functions.

A Stochastic Markov Model for Maintenance Scheduling in the Presence of Online Monitoring System

Active electricity distribution systems (AEDS) play an important role on the social welfare that is highly relies on the reliability of these systems. In this regard, maintenance management tries to decrease component failures via repairing component that are more possible to be failed in operation time. In this manuscript, a stochastic MINLP maintenance scheduling model is proposed in which the reliability of AEDS's equipment is considered through employing a three-state Markov model. This model helps us to embed the uncertainty of AEDS's equipment service restoration time by considering online monitoring system for it and FLISR functions in AEDS. In this paper, minimizing the total cost of AEDS is determined as objective function considering total customer interruption cost, total cost of energy not supplied, total cost of prevented maintenance actions, total corrective maintenance actions cost implemented in the AEDS and total cost of implementing online monitoring for AEDS's component in maintenance scheduling. The proposed framework is applied in two different cases, Case I is maintenance scheduling without online monitoring system and Case II is maintenance scheduling with online monitoring system. The aforementioned cases are implemented in an IEEE Test System as well as a real scale distribution network. Moreover, several sensitivity analyses are implemented for indicating the efficiency of the proposed model.

Feature extraction and health status prediction in PV systems

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains.

User-Centric BIM-Based Framework for HVAC Root-Cause Detection

In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants’ discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them.

Solving a parallel-line capacitated lot-sizing and scheduling problem with sequence-dependent setup time/cost and preventive maintenance by a rolling horizon method

This paper presents a novel Mixed-Integer Nonlinear Programming (MINLP) model for a parallel-line Capacitated Lot-Sizing Problem (CLSP) with the sequence-dependent setup time/cost, due date, and preventive maintenance planning. The target of this integrated model is to specify optimal lot sizes, inventory, and shortage levels along with the optimal preventive maintenance plan and the best sequence, starting time, and completion time of the lots. The production and scheduling aspect of the problem is modeled as the CLSP with the sequence-dependent setup time/cost and due date, while maintenance sub-problem is modeled by using the time-based maintenance technique and age-reduction modeling concept with the actual run-time and Exponential lifetime distribution. For the complexity of the model, the MIP-based Rolling Horizon (RH) heuristic algorithms are developed to solve the linearized model in a reasonable truncated time. The computational results obtained from generated small- to large-sized instances show that the proposed RH1 and RH2 heuristic methods report the final solutions with better quality (lower relative gap and total cost) compared with the solutions reported by the truncated simple CPLEX method. Especially, the RH2 method provides a reasonable production plan, better-adjusted schedules with respect to the due dates, and a proper PM plan in comparison with the other solution methods. Also, the featured model develops an optimal production, scheduling, and maintenance plan with higher feasibility, more available production time, and lower total cost in comparison with the classic CLSP with the sequence-dependent setups that neglects the effects of sudden failures and corrective maintenance actions.

Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data

Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions.

A maintenance strategy based on system reliability considering imperfect corrective maintenance and shocks

In this study, an innovative maintenance strategy is proposed for systems subject to normal degradation and shock process. The shock process is a non-homogeneous Poisson process, and a changing factor is considered for the dependence between the normal degradation and the occurrence intensity of shocks. The system reliability is divided into three stages, based on which maintenance activities are implemented. In Stage 1 and Stage 2, when a fatal shock occurs, corrective maintenance is performed if the number of completed corrective maintenance activities does not reach the allowed limit; Otherwise, the system is replaced. Replacement is always performed once the system reliability decreases to Stage 3. Corrective maintenance is imperfect, with a perfect or minimal effect at certain probabilities. Two control variables, the allowed numbers of corrective maintenance in Stage 1 and Stage 2, are determined to minimize the expected system cost rate, by using Particle Swarm Optimization algorithm. The proposed model and method are illustrated by an example of one sliding spool. The optimal result is verified by checking the results of feasible variables. Furthermore, the proposed maintenance policy is compared with two contrastive maintenance policies. The comparison results show the necessity and effectiveness of the proposed method.

A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies

Condition-based maintenance (CBM) optimization utilizing prognostic information and methods for complex systems has attracted increasing attention. This paper presents a bi-level approach to optimize a CBM policy for multi-component systems subject to stochastic and economic dependencies. The objective of the decision rule at the system level is to address if preventive and/or corrective maintenance actions are necessary regarding the predictive reliability of the system. At the component level, the aim is to identify the optimal group of components to be preventively maintained when maintenance is triggered due to the decision made at the system level. A maintenance efficacy indicator is introduced to prioritize components to be maintained by taking into account both the resulting reliability improvement quantity and the maintenance cost incurred. The optimal inter-inspection interval, the thresholds of system and component conditional reliability for triggering maintenance are jointly determined for minimization of the long-run average cost rate. A numerical study and sensitivity analysis on a 2oo3:G system are carried out to validate the performance of the proposed maintenance policy. Compared to another opportunistic maintenance policy without considering the system health condition, the proposed CBM policy leads to more cost gain and effectiveness in the maintenance decision-making.

EXPERT PANEL ON IN-SITU VISUAL INSPECTIONS FOR MASONRY CHURCHES MAINTENANCE STAGE

The incorporation of protocols in heritage building preservation is important for the definition of preventive conservation actions. Such integration is needed to avoid restoration actions and to promote preventive maintenance instead of corrective maintenance actions. This paper presents the application of an innovative digital management system using artificial intelligence that can quantify the suitability of a sample. This kind of application can support the maintenance management of buildings and minimise human error in data collection. The fuzzy system showed slight differences between the members of the expert panel during the in-situ visual inspection. These results indicate that, despite differences between various experts’ evaluation of a building, the proposed digital method helps minimise the uncertainty in the results. The paper highlights input variables, which present high dispersion (load state modification, fire and occupancy), and input parameters, which present low dispersion (preservation, roof design and overloads). Fuzzy systems can adequately manage the uncertainties associated with different experts’ assessment of sample that present constructive homogeneity. This study can give advantages to stakeholders during the inspection, diagnosis and evaluation stages in the improvement of mitigation policies focused on preventive maintenance programs dedicated to the resilience of heritage buildings, specifically churches emplaced in Chile.

Enhancing Productivity of CNC Machines by Total Productive Maintenance (TPM) implementation. A Case Study

Lean manufacturing is a business system that contributes to the growth of production efficiency and the company profit, by identifying and eliminating waste. TPM is one of the techniques used to eliminate production waste, being focused on the maintenance of production equipment, so as to increase their operation and efficiency, while ensuring the quality of production due to preventing the causes that lead to waste. In the present paper for the continuous improvement of the manufacturing process within the work cells equipped with CNC processing centers, TPM is implemented, which contributes to the optimization of predictive, preventive and corrective maintenance activities of production equipment, thus improving the efficiency of machine tools. TPM ensures maximum equipment efficiency due to the elimination of unforeseen equipment downtime, respectively increasing the speed and availability of the machine. The research is based on a case study from automotive industry. Monitoring the daily activities of the equipment is done through the TPM component, namely Overall Equipment Effectiveness (OEE) which presents the situation in the manufacturing process. The waste related to technological equipment were identified, such as: incorrect operation of the work equipment, unplanned shutdowns, respectively damages that occurred in the manufacturing process.

Mengukur Potensi Bahaya dan Resiko Kecelakaan Kerja dengan Metode Hazard and Operabillity Study pada Aktivitas Maintenance

Work accidents are things that must be avoided, so that's not inflicted loss on the company and the workers. However, several workers still not pay attention to occupational health and safety regulations and in the work area still found several potential hazards. The research is conducted at PT Pindo Deli Pulp and Paper Mills Indonesia in Karawang Regency and focuses on preventive and corrective maintenance activities that prevent paper machine breakdown. The purpose of this study is to measure how big the potential hazard and impact by using Hazard and Operability Study (HAZOP) method. This method is a systematic study, which aims to find the factors that causing work accidents and find out how big the potential accidents and their impacts. The data is collected by distributing questionnaires to the employee. The results show that hazards caused by the work area and the workers themselves are 18% and 82%, respectively. Based on the severity, the number of extreme, high, medium and low risk hazards are 1, 8, 1 and 1, respectively.

Simultaneous Planning of Production, Setup and Maintenance for an Unreliable Multiple Products Manufacturing System

The work presented in this paper addresses the problem of joint optimization of the production, setup and corrective maintenance activities of a manufacturing system. This system consists of a machine subject to breakdowns and repairs and producing two types of parts. A corrective maintenance strategy whose repair rate depends on the number of setup operations already performed on the production system is considered in this work. The objective of this research is to propose a policy that controls production, setup, and corrective maintenance. The contribution of this paper is through the control of the repair rate, combined with the planning of production and setup in a dynamic and stochastic context. Optimality conditions in the form of Hamilton-Jacoby-Bellman (HJB) equations are obtained and a numerical approach is proposed in order to deal with the joint optimization issues. Extensive simulations are performed to address many scenarios that illustrate the interactions between production, setup and maintenance activities.

Simultaneous Planning of Production, Setup and Maintenance for an Unreliable Multiple Products Manufacturing System

The work presented in this paper addresses the problem of joint optimization of the production, setup and corrective maintenance activities of a manufacturing system. This system consists of a machine subject to breakdowns and repairs and producing two types of parts. A corrective maintenance strategy whose repair rate depends on the number of setup operations already performed on the production system is considered in this work. The objective of this research is to propose a policy that controls production, setup, and corrective maintenance. The contribution of this paper is through the control of the repair rate, combined with the planning of production and setup in a dynamic and stochastic context. Optimality conditions in the form of Hamilton-Jacoby-Bellman (HJB) equations are obtained and a numerical approach is proposed in order to deal with the joint optimization issues. Extensive simulations are performed to address many scenarios that illustrate the interactions between production, setup and maintenance activities.