Inspection and performance analysis of crushing plant to improve crushing efficiency and ameliorate aggregate production for a quarry

PurposeThe purpose of this paper is to enhance the performance of maintenance in a solar power plant by implementing the proactive maintenance (PaM) strategy, measured by the availability and the total maintenance workload.Design/methodology/approachThe prior maintenance strategy was reviewed, and then the strategy was adjusted to focus on PaM. Failure modes and effects analysis (FMEA) was a tool for analyzing the severity and occurrence of the failure modes and effects. Then, the Why‒Why analysis was used for investigating the root causes of failures. The countermeasures were drawn, and the preventive maintenance (PM) plan was revised and carried out. The total maintenance, the PaM and reactive maintenance workload, was obtained, and then the improvements were determined. The values of availability were also obtained.FindingsPreviously, the appeared maintenance strategy was not clearly defined. It seemed to have reactive maintenance coupled with PM; it was checked once a year, and corrective actions were made when something wrong was found. Then the management team observed an increase in the reactive maintenance workload, whereas the values of availability were not consistent and tended to drop. After implementing the new maintenance strategy, PaM, the total maintenance workload decreased 14 percent in one year. The average availability of the solar power plant improved from 0.9943 to 0.9969, and the values of availability had better consistency.Practical implicationsThe PaM can be applied to solar power plant without limiting the prior maintenance strategy and the complexity of production or machinery. The solar power plant is a quite simple production, and most machines consist of electrical equipment and electrical circuits. The PaM supports to analyze the failure modes, the consequence of the failure events and failure effects, and to decide what should be done. Importantly, PaM can reduce total maintenance workload while the value of availability is higher and consistent.Originality/valueThis paper states how to successfully implement the PaM for the solar power plant. Previously, the plant did not have a clearly defined maintenance strategy; it was checked once a year, and it was corrected when abnormalities were detected. The PaM strategy provides tools and processes for failures and effects analysis. Although there was a more workload of PM, the total maintenance workload decreased, even in the first year.

This study presents a comprehensive analysis of the reliability and maintenance performance of a 1600-ton press machine in refractory production using an innovative inverse KPI model. Key metrics—including Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), a specially formulated Key Performance Indicator (KPI), and total downtime—were systematically evaluated over a 24-month observation period (June 2023-May 2025). The methodology involved the meticulous collection of monthly operational data, failure events, and repair logs. Time-series analysis was employed to identify trends and correlations between the implemented proactive maintenance strategies and equipment performance metrics. The inverse KPI, defined as KPI = (MTTR / MTBF) × 100, proved to be a highly effective tool for performance tracking. The results demonstrate a remarkable 75% reduction in total downtime by mid-2024, correlating directly with a significant increase in MTBF and a decrease in the KPI value. A temporary performance anomaly in early 2025 was investigated and linked to an unforeseen component failure, highlighting the importance of continuous monitoring. The findings conclusively demonstrate the efficacy of a data-driven, proactive maintenance approach, providing a practical and transferable framework for enhancing industrial asset management. This study underscores the substantial benefits of applying systematic reliability engineering principles to optimize performance in traditional industrial settings.

Purpose: This study explores the application of Discrete Event Simulation (DES) using FlexSim software to enhance the operational efficiency of a Liquefied Petroleum Gas (LPG) bottling plant. The primary goal is to ensure that the LPG plant can safely and efficiently meet escalating market demands, thereby prioritising all stakeholders' safety. Design/Methodology/Approach: The research design focused on empirical research and experimental simulation modelling. The study began with collecting and analysing one month of LPG plant data, laying the foundation for developing a simulation model. Verification and validation processes ensured the model's accuracy, enabling the investigation of various operational scenarios. The key performance indicators like First Time to Failure (FTTF), Time Between Failures (TBF), and Time to Repair (TTR) were analysed. The availability rates were 77% from actual data and 76% from simulations, showing that the model is suitable for real-world use. Findings: This study's findings underscore the potential impact of proactive maintenance strategies and operational enhancements as practical and applicable approaches to optimising performance. The analysis also revealed significant improvement opportunities through what-if scenarios: increasing MTBF by 100%, reducing MTTR by 50%, and raising conveyor speed by 15%. Research Limitation: The study's dependence on a systematic literature review could restrict its ability to capture the industry's real-time dynamics. Practical Implication: Implementing proactive maintenance strategies and operational enhancements as practical approaches can reduce downtime and costs and promote productivity and safety. Social Implication: Optimising plant operations will help maintain supply chain stability with the growing demand for LPG. Originality/Value: This work contributes valuable insights and recommendations, establishing a foundation for informed decision-making in the LPG bottling sector.

This empirical study rigorously investigates the impact of transitioning from a reactive maintenance strategy to a proactive approach within the context of power generation companies. The central aim is to quantify and provide a comparative analysis of the efficiency, cost implications, and overall operational impact of adopting proactive versus reactive maintenance strategies in a power plant setting. Drawing on meticulously collected data, the research considers an array of key performance indicators, including maintenance costs, equipment breakdowns, downtime duration, total power output, equipment lifespan, safety incidents, regulatory compliance violations, and investment in staff training and predictive maintenance tools. The findings of this study are both revealing and quantitatively substantial. A transition to a proactive maintenance strategy has demonstrated a reduction in maintenance costs by approximately 20%, coupled with a 35% decrease in the number of equipment breakdowns. Downtime duration was significantly reduced by 40%, enhancing operational efficiency and power output. Notably, the total power output increased by 15%, and the equipment lifespan was extended by an average of 25%. Furthermore, a marked decrease of 50% in safety incidents was observed, reflecting the profound impact of proactive strategies on enhancing safety protocols. However, these improvements are juxtaposed with an initial investment surge, where staff training costs increased by 30%, and expenditure on predictive maintenance tools rose by 25%. This research underscores the critical importance of a comprehensive and quantified understanding of maintenance strategies and their broader impacts on power plant performance. The study illustrates that while proactive maintenance demands initial investments, the long-term benefits significantly outweigh these costs, leading to enhanced operational efficiency, safety, and cost-effectiveness. The insights gleaned from this study provide invaluable guidance for power plant operators, stakeholders, and policymakers in their pursuit to optimize operations, improve safety standards, and achieve economic efficiencies, thereby advocating for a strategic shift towards more proactive maintenance approaches in power plant operations.

In this paper, we introduce three proactive maintenance strategies for static Wireless sensor Networks (WSNs) using a limited number of mobile maintainer robots: the Centralized Proactive Maintenance Strategy (CPMS), the Fixed Distributed Proactive Maintenance Strategy (FDPMS) and the Adaptive Distributed Proactive Maintenance Strategy (ADPMS). The proposed maintenance strategies are based on a simple analytical energy dissipation model to estimate the occurrence times of the expected sensor failures in the network. Once identified, the anticipated failures are replaced by the available robots before they happen. Simulation results have shown that CPMS gives the minimal network dysfunction time representing the interruption time of the service provided by the network. But, due to its significant signaling cost, we have remarked that CPMS can be deployed only in small scale WSNs. In large scale ones, we recommend using the ADPMS maintenance strategy. However, in particular cases, when sensor failures are uniformly distributed on the network map, FDPMS has given the best performances.

The nodes in wireless sensor networks (WSNs) are responsible for communicating data which is primarily of three types viz. video, audio and text. In literature, a large number of energy aware and shortest path based route selection approaches have been proposed to enhance the routing efficiency in WSNs. Most of these route selection approaches are designed by assuming fixed type and size of data packets and results in the same path selection for all types of data. This path selection, does not consider data size and data priority, results in high contention losses, topology failures and non-uniformity in energy depletion of nodes. In this paper, a route selection approach which is based on the preservation of network connectivity for improving overall network lifetime has been proposed for applications where sensor nodes are required to transmit different types of data. The proposed approach preserve highly connected edges at the initial rounds of data communication in an energy efficient manner, such that network connectivity would be maintained even at later rounds of data communications. The proposed route selection approach, VDR, (Variable Data rate Routing) has been simulated with both proactive and reactive route maintenance strategies. The simulation results show that the proposed algorithm with both proactive and reactive route maintenance strategies result in better network lifetime, energy consumption, overall network connectivity, packet delivery ratio as compared to the existing State-of-the-Art energy efficient route selection approaches with proactive and reactive route maintenance strategies. The results also show that the proposed algorithm is scalable and performs better than the compared algorithms both at low node density and high node density.

In this paper, we propose proactive maintenance strategies useful to preserve the coverage of a Wireless Sensor Network WSN and protect its connectivity from the eventual sensor failures. Failures are handled before they happen. To achieve this goal, we introduce an analytical model to represent the energy dissipation of a node sensor. The proposed model is useful to estimate the life time of each sensor in the network. Once identified, the anticipated faulted sensors are replaced with new ones by a set of mobile robots. The maintainer robots are scheduled using two different approaches. The Heuristic Centralized Proactive Maintenance Strategy HCPMS uses a centralized approach to schedule a set of maintainer robots when dealing with the expected sensor failures. However, the Fixed Distributed Proactive Maintenance Strategy FDPMS and the Market based Distributed Proactive Maintenance Strategy MDPMS use distributed algorithms to select and designate the maintainer robot of each anticipated failure. Simulation results show that HCPMS is adapted only for the small-scale WSNs. However, MDPMS can provide a good compromise between the communication cost and the provided network dysfunction time in large-scale ones.

Linking maintenance strategies to performance

Among various modes of transportation, maritime transportation holds critical importance since it provides substantial carrying capacity with low unit costs. To perform seamless and efficient operations in maritime transportation plays a pivotal role in achieving sustainable development goals and the International Maritime Organization (IMO) targets. The execution of uninterrupted operations can only be carried out with the existence of reliable systems. Creating reliable systems onboard is possible through the implementation of planned and proactive maintenance strategies and leveraging experiences gained from past failures. 10-year failure records of bulk carriers have been scrutinized within the scope of system reliability to determine critical equipment and units. The data has been categorized into subgroups under four fundamental headings, and subsequent reliability analyses have been conducted on each subgroup. Within the subgroups, the reliability of navigation equipment should be improved since it has the highest failure rate and its malfunction can cause very serious marine accidents. This equipment is followed by fire-fighting systems, cargo equipment, and GMDSS instruments which are essential for ship operations based on reliability results. Therefore, regular failure records, planned and proactive maintenance strategies, and also extra efforts should be performed on this equipment to ensure sustainable and seamless operations in the maritime sector.

ABSTRACTIn this paper, we have introduced three proactive maintenance strategies for static wireless sensor networks (WSNs) using a limited number of mobile maintainer robots: the centralised proactive maintenance strategy denoted by CPMS, the fixed distributed proactive maintenance strategy denoted by FDPMS and the adaptive distributed proactive maintenance strategy denoted by ADPMS. The proposed maintenance strategies are based on a simple energy dissipation analytical model to estimate the occurrence times of the expected sensor failures in the network. Once identified, the anticipated failures are replaced by the available robots before they happen. To select the appropriate maintainer robot for each expected failure, CPMS opts for a centralised scheduling method based on the genetic algorithm fundamentals. However, the distributed maintenance strategies (FDPMS and ADPMS) use two different WSN area partitioning methods to share the sensor maintenance tasks among the available robots. Simulation results have shown that CPMS gives the minimal network dysfunction time representing the interruption service time induced by the detected faulted nodes. However, due to its significant signalling cost, we have remarked that CPMS can be deployed only in small scale WSNs. In large‐scale ones, ADPMS has demonstrated its efficiency in terms of the network dysfunction time, the robot travelled distance and the introduced signalling cost. In particular cases, when sensor failures are uniformly distributed on the network map, FDPMS has given the best performances. Copyright © 2013 John Wiley & Sons, Ltd.

The aim of this paper is to propose an enterprise modelling-based formal framework Architectures for Enterprise Integration (London: Chapman & Hall), Vernadat 1996, Enterprise Modeling and Integration, Principles and Applications (London: Chapman & Hall)], as a common model of understanding between all the actors involved in a proactive maintenance strategy implementation. This enterprise modelling-based formal framework extends GERAM and CIMOSA modelling frameworks to integrate, from the shop-floor level to the business one, the three basic \\[Bernus et al . 1996, processes of prognosis, diagnosis and monitoring which make up a proactive maintenance system. This extension is mainly based, first, on the systemic paradigm taking into account some other principles (e.g. modularity or mapping mechanism); and second, on the federation of scientific and normative works related to maintenance processes (i.e. model-based diagnosis, IEC 191- 1990, etc.). This formal framework has been applied within the European ESPRIT IV REMAFEX project to two hydropower plants for maintenance system modelling. The results of these methodological and applied researches from PhD thesis (Le´ger 1999, Methodological contribution to the proactive maintenance of the industrial production systems: proposition of a formal modelling framework. PhD thesis of the Nancy University Henri Poincaré, France) have been the roots to start up the innovative PREDICT company for consulting, training and software development in proactive maintenance area.

This paper studies the use of fuzzy logic theory embedded in fuzzy axiomatic design to develop an integrated model with the analytical hierarchy process, and a selection of maintenance strategies in manufacturing systems through weighted sum-product evaluation. In this paper, an analysis was conducted to understand the model characteristics of combined fuzzy axiomatic design, analytic hierarchy process and weighted aggregated sum product assessment. The results from the analysis served as information to establish the best maintenance strategy. Based on the data obtained from a factory, which was tested on the proposed framework, it is concluded that the most preferred proactive maintenance strategy for the case study was preventive maintenance, followed by reliability-based maintenance while predictive maintenance is the least preferred maintenance strategy in the rolling mill. Accordingly, the two-stage fuzzy multi-criteria maintenance strategy selection approach is appropriate to select the best maintenance strategy for the factory. This paper offers a new method to establish the best maintenance strategy for a rolling mill. As such, the manager could install a method for significant improvement in engineering practices with promising business excellence and competitiveness results.

The increasing complexity of urban infrastructure necessitates more efficient and proactive maintenance strategies. Traditional maintenance approaches often rely on reactive measures, leading to increased costs, unplanned downtime, and potential structural failures. The emergence of Artificial Intelligence (AI)-driven Digital Twin technology offers a promising solution by enabling predictive maintenance through real-time monitoring and advanced analytics. This study aimed to evaluate the effectiveness of AI-driven Digital Twin systems in enhancing predictive maintenance for urban infrastructure. A qualitative case study methodology was employed, analyzing multiple infrastructure projects that integrated Digital Twin technology. Data were collected from project reports, real-time sensor outputs, and expert interviews. The predictive capabilities of machine learning models, including Decision Trees, Support Vector Machines (SVM), and Deep Learning networks, were assessed based on their precision, recall, and F1-score. The results demonstrated that Deep Learning models achieved the highest fault detection accuracy, with an F1-score of 92.5%, outperforming other models. The adoption of Digital Twin systems resulted in a 30% reduction in maintenance costs and a 40% decrease in infrastructure downtime. Additionally, AI-driven predictive maintenance improved fault detection efficiency, reducing the average detection time from 15 days to 3 days. These findings highlight the potential of AI-enhanced Digital Twins in optimizing urban infrastructure resilience, cost efficiency, and sustainability. This study underscores the importance of integrating AI and Digital Twin technologies in predictive maintenance strategies. Future research should focus on addressing implementation challenges, including data security, interoperability, and computational costs, to facilitate broader adoption in smart city development

Instrumentation & Control Systems have gone through various revolutions in the oil and gas industry. The start of the industry was based on pneumatic controllers, operating with instrument air or instrument gas at 0.2 to 1.0 barg and the Instrument Protective System (IPS) was relay based. This system provided almost zero information or data that can be used to predict failure and had a higher unrevealed failure. The next phase of instrumentation was migration into the electronics era. This allowed for the migration from a pneumatic system to the application of electronics field device operation on 4-20mA connected to Distributed Control System (DCS) and IPS. Further development of 4-20mA communication protocol allowed for the development of digital superimpose communication called Highway Addressable Remote Transducer (HART). The data that is transmitted in HART protocol provides sufficient data and information to predict the health and functionality of the instrumentation. Changes in the maintenance philosophy from reactive maintenances to proactive or predictive maintenances, resulted in reduced downtime by scheduling maintenance to optimize the working window. This work process allows for greater productivity of assets, both human and capital. Dashboards are developed and utilised to alert fault detection and loss of redundancy, whereby data is relayed from DCS and IPS to a web-based system. Available tools being developed in the industry and application of IoT within the industry allows for real-time field device health monitoring and an engine to predict deviations. The strength and weakness of the various proactive or predictive maintenance strategies are compared and summarized, including scope for future research and application in the industry.

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.