Preventive Maintenance Research Articles

Opportunistic maintenance for a novel load-sharing system with dependent degradation rate and volatility

For traditional degrading load-sharing systems, a typical assumption lies in the only effect of unit load on its degradation rate. However, motivated by a laser system example with positive correlation between degradation rate and volatility, we present a novel load-sharing system with general degradation rate-volatility-load correlation. Furthermore, to take account for unit economic dependence caused by sharable maintenance set-up cost, and the window period generated by the maintenance on some unit, opportunistic maintenance (OM) strategy is introduced to offer the opportunity of simultaneously maintaining another working unit under control-limit criteria. Based on the semi-regenerative process, the long-run average cost rate is minimized to obtain the optimal inspection interval, preventive maintenance and OM thresholds. Compared to the traditional condition-based maintenance strategy separately for each unit, the effectiveness of OM strategy is validated through numerical examples and case studies, offering broad managerial insights for engineers to reduce maintenance expenses.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

Failure evaluation of outdoor polymeric insulators based on statistical condition-assessment methods

Field aging of outdoor polymeric insulators results in unscheduled outages in power systems particularly in harsh climate conditions. In this paper, a strategic maintenance framework is proposed in order to prevent overhead line outages. The presented scheme is based on condition assessment of the real aged polymeric insulators. Data of tensile test, hardness test, thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) are used to extract critical features of the aged samples based on classification of fuzzy c-means clustering. Then, a lifetime prediction analysis is done based on Cox hazards analysis to develop further maintenance strategy. The proposed model is validated through numerical analyses on polymeric insulators for various validation evaluations including pollution severity analysis and hydrophobicity class (HC) measurement. Obtained results verify accuracy of the intelligent model to predict lifetime of the aged polymeric insulators. The presented strategic framework can be effectively applied to online monitoring, condition assessment and preventive maintenance programs of the power system utilities.

A novel performance prediction method for harmonic reducers based on the trend-enhanced Fisher’s discriminant ratio-Wiener process

Abstract Harmonic reducers, as core components of industrial robots, play a critical role in maintaining robot health. Performance degradation assessment (PDA) and remaining useful life (RUL) prediction are essential for ensuring the operational reliability of such robots. To address the multistage characteristics and stochastic uncertainties in the performance degradation of harmonic reducers, a performance prediction method based on Fisher’s discriminant ratio and Wiener process is proposed. Firstly, the health indicator (HI) construction is defined as an optimization problem based on Fisher’s discriminant ratio and trend monotonicity constraints. By leveraging the sum of the multiplication of frequency amplitudes and optimized weights, precise segmentation of the multistage degradation process over the entire lifecycle is achieved. Notably, the optimized weights can automatically identify the resonance frequency bands caused by damage. Subsequently, a nonlinear Wiener process model with a drift coefficient in the form of a power function is established. The probability density function (PDF) expression for RUL is derived based on the concept of first hit time (FHT). Additionally, a logarithmic likelihood function (Log-LF) for the unknown parameters in the degradation model is constructed. The experimental results indicate that the proposed method surpasses the other two Wiener process models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), and cumulative relative accuracy (CRA). This provides robust support for preventive maintenance decision-making for harmonic reducers.

Functional assessment of pavements on a university campus

With the constant increase in traffic volume at the Darcy Ribeiro Campus of the University of Brasília (UnB), attention to the preservation of road infrastructure is necessary. The implementation of an Urban Pavement Management System (UPMS) emerges as a strategy. This study aims to evaluate the functional condition of the pavements at the UnB University Campus by observing distresses on their surfaces, using the Fixed Value Matrix for 5 Distress method by Zanchetta (2017). A total of 161 road segments were evaluated, and both Objective and Subjective Pavement Condition Index (PCI) values were obtained, along with the recommended intervention for each segment. The most common types of defects found in the pavements were Raveling and Fatigue Cracks. The most suggested interventions were Preventive Maintenance (23.60%) and Corrective Maintenance (22.98%). Statistical analysis led to the conclusion that there were no significant differences between the Objective and Subjective PCI data. Thematic maps were generated for the Objective PCI, Subjective PCI, and maintenance activities in order to make georeferenced spatial information available.

A Hybrid Method for Identifying Critical Failure Modes in a Ball Mill

The reliability of a ball mill is crucial for the seamless operation of ore processing and other industrial plants, where unexpected equipment failure or downtime can result in significant financial losses and reduced production efficiency. Thus, this paper examined a hybrid method for identifying critical failure modes in a ball mill using the Failure Modes, Effects, and Criticality Analysis combined with the Complex Proportional Assessment with Grey Numbers (COPRAS-G). The COPRAS-G method was employed to assess the criticality of various failure modes by determining the weights of significant failure modes in the bearing, gearbox, motor, and shaft components of a ball mill. It was observed that gear seal failure is the most critical failure with percentage contribution (Ni) of 100%, while bearing wear is the least critical failure with percentage contribution (Ni) of 62%. The result suggests that failure modes of gear seal failure, gear pitting, motor bearing fatigue, and shaft fracture are regarded as the main contributors to failure with percentage contribution (Ni) of 100%, 97%, 87% and 83% respectively. Current ball mill maintenance includes corrective, preventive and predictive maintenance. For failure modes with high criticality, predictive maintenance was advised, while for moderate and low criticality, corrective and preventive maintenance were advised.

An aero-engine remaining useful life prediction model based on clustering analysis and the improved GRU-TCN

Abstract Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA-DRGRU-TTCN, aimed at accurately predicting the RUL of engines. Firstly, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by JS divergence. Secondly, the deep connectivity of the residual Gated Recurrent Unit (GRU) module is designed to extract deeper degradation features, and an improved TMSE loss function based on the first degradation time point is applied to Temporal Convolutional Networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C-MAPSS dataset show that the proposed method achieves better performance compared to existing state-of-the-art methods.

A novel TD3 for solving multi-level imperfect maintenance optimization problem

Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.

A holistic approach to kitting cart optimization and steel receiving analysis for process improvement

This paper investigates the optimization potential within the steel-receiving process and kitting cart management in modern manufacturing facilities. As evidenced by a 20% improvement at Asuruf Industrial, the study indicates how the increasing popularity of kitting, with its capacity to reduce assembly time and space through pre-sorted kits, synergizes with vital efficiency gains in steel receiving. The implementation of Lean manufacturing tools, coupled with strategic interventions, yielded significant operational enhancements, resulting in an annual savings of $121,143. This success reinforces that structured operational strategies and Lean tools are indispensable for elevating manufacturing productivity and profitability. Anticipating a 45 to 50% increase through automation and preventative maintenance, the study underscores ongoing improvements in the steel-receiving process, emphasizing the crucial role of teamwork and Lean principles in addressing industry challenges. This approach not only ensures heightened productivity and competitiveness in the manufacturing sector.

Importance de l’analyse vibratoire sur la maintenance préventive des réducteurs planétaires dans une cimenterie: « cas de la cimenterie de Lukala dans la province du Kongo Central en R.D. Congo »

Planetary gearboxes are essential for reducing the rotational speed of electric motors to a suitable level for cement production equipment, such as grinders and kilns. By integrating a preventative maintenance strategy for these reducers, the cement plant can minimize unplanned downtime, optimize performance and extend the useful life of the equipment. This involves regular inspections, replacing worn parts before they fail, and properly lubricating components to ensure optimal operation. By investing in effective preventative maintenance, cement plants can save money in the long term by avoiding high costs associated with emergency repairs and production interruptions. The impact of a planetary gearbox in a cement plant therefore becomes significant.

Prediction of cavitation using machine learning techniques on centrifugal pump

Abstract When the local static pressure drops below the fluid’s vapour pressure at that particular temperature, a dynamic process known as cavitation takes place in the liquid. Due to cavitation, the performance (total head and flow rate) of the pump will deteriorate, and hence the vibration and noise will develop. Both the performance and cavitation investigations are carried out for a low-specific-speed pump as per the testing standards. The results obtained during cavitation studies are extracted to train the machine learning algorithms. The flow rate, speed, torque, suction head, and delivery head are considered as input variables for machine learning, with total head, Net Positive Suction Head (NPSH), efficiency, and noise magnitude being output variables. The current studies employ four machine learning techniques: random forest, support vector machines, extreme gradient boosting, and linear regression. In the analysis, notable performance was observed for the random forest and extreme gradient boosting algorithms among others consistently demonstrated superior predictive capabilities for the output parameters of the pumps. For linear regression, random forest, and extreme gradient boosting, the coefficient correlation values are around one for all output parameters except for noise. Similar observation is depicted for coefficient of determination and normalized root mean squared error. These ensure the level of noise during cavitation is very dynamic. These findings will enable the pump user to incorporate AI techniques for the diagnosis of centrifugal pump operation with reference to the nominal flow rate (best efficiency flow). Through this technology, preventive and predictive maintenance of pumps can be adapted with less downtime in process industries, power plants, chemical industries, oil and gas refineries, and so on.

Preventing Chemical Corrosion

When it comes to processing chemicals, pumps are on the front line and potentially under attack from the product. How can preventive and predictive maintenance help deliver smooth operations in often volatile conditions?

Condition-based maintenance policy for shared service-oriented leased equipment

Due to growing consumer demands for immediacy, personalization, and high availability, equipment manufacturers are venturing into the sharing market with expanded shared leasing services. While selling usage rights of equipment, they provide additional services to the consumer in the role of equipment operators, such as free maintenance and swap. However, uncertain usage rates and return times in the shared model pose significant maintenance challenges. This paper proposes a personalized condition-based maintenance (CBM) policy accounting for these uncertainties and heterogeneous consumer usage patterns. By considering inspection, maintenance, and penalty costs, we minimize the long-run expected maintenance cost per unit of time for operators. In the numerical example, we compare our policy against fixed inspection interval CBM policy and non-fixed inspection interval CBM under average usage rate, demonstrating that personalized CBM reduces maintenance costs. Sensitivity analysis provides some managerial insights. Firstly, operators should adjust the number of free swaps strategically to manage the intensity of equipment swaps, and strike a balance between the increased maintenance costs due to the swap intensity enhancement and the improved user satisfaction and equipment availability resulting from the increased number of free swaps. Secondly, when the acceleration effect of imperfect maintenance on the degradation rate is strengthened, operators should prioritize reducing the frequency of imperfect maintenance and increasing the frequency of perfect preventive maintenance. Moreover, operators must make strategic investments in their maintenance actions, balancing maintenance costs and penalty costs to develop cost-effective imperfect maintenance improvement factors.

Integrated optimization of production and maintenance scheduling with third-party worker resource constraints in distributed parallel machines environment

Outsourcing machine maintenance to third parties has been a trend due to the increasing complex of machines and the benefits of maintenance outsourcing. However, this new phenomenon is ignored in previous studies pertaining to the integrated optimization of production and maintenance scheduling (IOPMS) problems, resulting the lack of theoretical guidance for production managers to formulate optimal scheduling schemes under this new trend. In this study, we investigate an IOPMS problem considering maintenance outsourcing in a distributed parallel machine environment, referred as IOPMSTW, which requires the use of third-party worker resources to perform preventive maintenance. Makespan and total cost are two optimization objectives. We first formulate the problem by developing a mixed integer linear programming. Then a memetic algorithm incorporating iterated greedy method (IG) is proposed to solve the IOPMSTW, in which an improved decoding method and a problem-dependent local search operator based on IG are designed to respectively ensure the feasibility of new generated individuals and improve the searching efficiency. The validity of proposed mathematical model is verified by CPLEX based on eight small instances. Based on 240 constructed instances, a set of comprehensive experiments are conducted. The results demonstrate that the local search operator improved the searching performance of the proposed algorithm by 100%. Comparison results with other well-known algorithms show that the proposed algorithm achieved the best results on more than 85% of the tested instances.

Sparse Graph Structure Fusion Convolutional Network for Machinery Remaining Useful Life Prediction

Effective prediction of machinery remaining useful life (RUL) is prominent to achieve intelligent preventive maintenance in manufacturing systems. In this paper, a sparse graph structure fusion convolutional network (SGSFCN) is proposed for more accurate end-to-end RUL prediction of machine. A novel node-level graph structure called time series shapelet distance graph (TSSDG) is designed to convert the time series to node feature. The SGSFCN model is proposed to learn degradation information from the graph structure. In SGSFCN, a sparse graph structure (SGS) layer and a fusion graph structure (FGS) layer preceding the graph convolutional network (GCN) are designed to learn the SGS from node representation and fuse the original graph structure, enabling the graph structure and node update iteratively in subsequent layers. Concurrently, a bidirectional long short-term memory network (BiLSTM) layer is integrated to capture the global temporal dependencies. The method is validated by two test rig data, and results demonstrate that the proposed method offers significantly higher prediction accuracy of RUL compared to several state-of-art methods.

Study of Variation in Physiochemical Properties of a Worm Gearbox Lubricant by Blending Castor Oil in the Base Lubricant

In both industrial & heavy-duty applications, oil analysis can be a very effective strategy for preventing gearbox failure, cutting downtime, and managing maintenance costs. In any application, gearboxes are essential to production, the unplanned downtime and the resulting productivity losses can prove to be extremely expensive. Used oil analysis is a preventative maintenance procedure that can point out and pinpoint mechanical breakdown inside the system before it gets serious or becomes too expensive to fix. A thorough oil examination can identify wear, corrosion, and other changes that could be harmful to the health and durability of the machinery. To reduce machine wear, prevent contamination, or optimize working conditions in challenging environments, lubricants are carefully formulated by mixing additives in the base oil. A lubricant with a high acid content can cause varnish and sludge to accumulate, which can clog oil filters and cause machine parts to corrode. When a lubricant degrades, acidic byproducts are produced because of the base stock and additives' process of decomposition in the presence of heat and air. To assess the presence of acidic constituents in the oil or its acid concentration, Total Acid Number (TAN) oil testing is employed. The present work studies the effects on Total Acid Number (TAN) and Total Base Number (TBN) properties of commercial lubricant (EP 90) when castor oil is mixed in percentage concentration in it. The findings of this research have unveiled that the acidic characteristics of the base lubricant (EP90 commercial gear oil) increases as the percentage of castor oil additive is increased in the base lubricant oil.

Application of the Acousto-ultrasonics technique to bridge cables in their anchorages.

The objective of this research is to evaluate the state of health of civil engineering cable structures using the acousto-ultrasonics method. More specifically, the element to be examined is the cable in its non-accessible part inside the anchor base. Cables in anchor caps are sensitive areas subject to various types of damage, such as water infiltration, which causes corrosion inside the anchor, or fretting fatigue. Such degradation cannot currently be assessed using NDT techniques. A number of challenges need to be overcome, including the complex geometry of the anchors, which limits the installation of sensors, the diversity of damage that may be present, the sensitivity to the defects being sought and the reproducibility of the measurements. Once the acoustic signals have been acquired, data analysis is crucial, requiring the use of unsupervised or supervised techniques such as principal component analysis, or more advanced techniques such as decision tree forests. These approaches aim to determine the state of health of anchor caps from the analysis of acoustic signals, providing a reliable diagnostic for the assessment of these parts of engineering structures. This development could have major implications for the preventive maintenance and sustainability of cable infrastructures, by giving us a reliable and accurate method for assessing cable state.

Advancing predictive maintenance: a comprehensive case study through industry 4.0

The paper is focused on predictive maintenance in the automotive industry with a specialization in technologies related to Industry 4.0. The emergence of predictive maintenance with Industry 4.0 technologies is also called Maintenance 4.0. The article describes specific Industry 4.0 technologies, such as supervisory control and data acquisition (SCADA) and Predictive Maintenance (PdM), that are being implemented in manufacturing companies. The sample for the case study consisted of a paint shop from the automotive sector that operates in Turkey. In the data collection step, SCADA collected data from pressure sensors to create a historical database for PdM. The purpose of this article is to illustrate, through a case study, the importance of PdM using SCADA using Industry 4.0 instruments such as the Internet of Things and big data. 18 filters, which were replaced every 6 months during preventive maintenance, are now replaced at different intervals through PDM after this study. Based on SCADA data, a total of 2 filters experience blockage in both the primer and topcoat paint processes every 6 months. Over a 12-month period, an extra 9 filters are blocked across all processes. Furthermore, within a 24-month timeframe, a total of 7 more filters become blocked in the entirety of the operational processes. The results showed that the PdM combination increased the effectiveness of care by 45.83%.

Power Distribution Transformer Maintenance Skills Required byElectrical Engineering Technology Students ofPolytechnics inNorth-East Nigeria

Electric power distribution industry in North-East, Nigeria is faced with inadequate number of power transformer maintenance personnel which has led to numerous abandonment of broken down power transformers. Transformer faults have been blamed for many power outages of long duration in many towns and communities in North-East, Nigeria.It is against this backdrop that thisstudy determined the power distribution transformer maintenance skills required by Electrical Engineering Technology students of polytechnics in North-East Nigeria. Three research questions and three hypotheses guided the study which employed a descriptive survey research design. The population of the study was 144 comprising of 135 Electrical Engineering Technology Lecturers, 3 Power Transformer Maintenance Technicians and 6 Electric Power Distribution Company-Based Supervisors. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Power Distribution Transformer Maintenance Skills Required Questionnaire (PDTMSRQ) developed by the researchers. The instrument was validated by three experts and a reliability of 0.74 was obtained using Cronbach Alpha reliability method. Mean statistic was used to answer the research questions while ANOVA was used to test the two null hypotheses of the study at 0.05 level of significance. The findings of the study revealed that the Electrical Engineering Technology students of polytechnics in North-East Nigeria required preventive, predictive and corrective maintenance skills. The study recommended the following: Lecturers should encourage the students’ skill acquisition process by exposing them to various maintenance activities in order for them to imbibe the different methods of power transformer maintenance; Students should be trained to recognize sounds and sights of faulty equipment in order to predict the parts or components that needed replacement or repairs.

Multi-Criteria Decision-Making Framework (AHP-TOPSIS): Pavement Preventive Maintenance Case Study for Ordinary National Trunk Highways

Pavement maintenance and rehabilitation decision-making needs to weigh multiple strategic goals to achieve sustainable development through the pavement maintenance management system. Making decisions regarding pavement preventive maintenance is both intricate and costly. This study introduces a multi-criteria decision-making framework aimed at enhancing the scientific basis of such decisions. The framework first establishes an evaluation system for preventive maintenance strategies by considering three primary evaluation criteria—service functionality, pavement performance, and economic benefits, and then identifies nine specific evaluation indicators to influence these criteria, with a comparison matrix constructed to determine the weight of each indicator in relation to the maintenance decision hierarchy. Following this, the technique for order preference by similarity to ideal solution (TOPSIS) is employed to prioritize four commonly utilized preventive maintenance strategies. The results reveal that pavement condition and maintenance costs are the most influential factors in determining the appropriate preventive maintenance strategies for national highways. The priority rankings for the four strategies—slurry seal, micro-surfacing, chip seal, and ultra-thin overlays—are found to be 56.12%, 63.86%, 12.12%, and 83.52%, respectively, with ultra-thin overlays identified as the optimal choice for second-class highways. The decision-making model utilized in this study enables a multi-dimensional analysis, reducing the subjectivity inherent in expert evaluations and facilitating the prompt identification of the most suitable maintenance strategy.