Computerized Maintenance Management System Research Articles

Development and Usability Evaluation of VulcanH, a CMMS Prototype for Preventive and Predictive Maintenance of Mobile Mining Equipment

This paper details the design, development, and evaluation of VulcanH, a computerized maintenance management system (CMMS) specialized in preventive maintenance (PM) and predictive maintenance (PdM) management for underground mobile mining equipment. Further, it aims to expand knowledge on trust in automation (TiA) for PdM as well as contribute to the literature on explainability requirements of a PdM-capable artificial intelligence (AI). This study adopted an empirical approach through the execution of user tests with nine maintenance experts from five East-Canadian mines and implemented the User Experience Questionnaire Plus (UEQ+) and the Reliance Intentions Scale (RIS) to evaluate usability and TiA, respectively. It was found that the usability and efficiency of VulcanH were satisfactory for expert users and encouraged the gradual transition from PM to PdM practices. Quantitative and qualitative results documented participants’ willingness to rely on PdM predictions as long as suitable explanations are provided. Graphical explanations covering the full spectrum of the derived data were preferred. Due to the prototypical nature of VulcanH, certain relevant aspects of maintenance planning were not considered. Researchers are encouraged to include these notions in the evaluation of future CMMS proposals. This paper suggests a harmonious integration of both preventive and predictive maintenance practices in the mining industry. It may also guide future research in PdM to select an analytical algorithm capable of supplying adequate and causal justifications for informed decision making. This study fulfills an identified need to adopt a user-centered approach in the development of CMMSs in the mining industry. Hence, both researchers and industry stakeholders may benefit from the findings.

Creation of a Computerized Maintenance Management System (CMMS) with Features and Planning Requirements

Creation of a Computerized Maintenance Management System (CMMS) with Features and Planning Requirements

CMMS- Streamlining Maintenance management for Food and Beverage Industry to capture breakdowns

CMMS stands for Computerized Maintenance Management System. In today’s world, when technology is advancing each day at an astonishing rate, any organization must have a detailed knowledge and understanding of its current state of machinery, the number of resources spent on the production floor to maintain equipment reliability, and resource allocation. CMMS is a critical and powerful tool that assists and helps any manufacturing industry to make full use of its capabilities, manage or direct workforce, improve the machine reliability, find innovative ways to improve productivity, stay in compliance with food and dietary regulatory standards as per the safe quality foods (SQF) and national sanitation foundation (NSF) standards and achieve series of goal and benefits the organization overall. Today, the food and beverage industry has grown tremendously and is the backbone of the manufacturing industry. Every company looks at creative ways to help them excel by selecting a reliable CMMS that suits their operations and processes to make educated decisions by monitoring their KPIs. This theoretical study aims to provide a building framework to set up plant maintenance modules to manage work orders, including emergency breakdowns and scheduled maintenance repairs in the food and beverage industry.

Integrating Parabolic Trough Solar Fields Into Computerized Maintenance Management Systems

Computerized maintenance management systems for power plants are not well suited for application in solar fields. They work well for tracking maintenance requirements on the hundreds or even thousands of pieces of equipment in conventional power plants, but they do not work as well tracking the maintenance requirements on hundreds of thousands of identical pieces of equipment found in a solar field. A database application, known as a field status program, is being developed for tracking the maintenance and status of collectors and components in parabolic trough solar fields as a complement to the computerized maintenance management system. This system, patterned after a similar system developed by the KJC Operating Company in the 1990s, provides a valuable tool for the O&M of parabolic trough solar fields. This paper describes the development and application of the tool and the new expanded capabilities that are being added to further enhance the O&M of solar fields.

Unlocking maintenance insights in industrial text through semantic search

Maintenance records in Computerized Maintenance Management Systems (CMMS) contain valuable human knowledge on maintenance activities. These records primarily consist of noisy and unstructured texts written by maintenance experts. The technical nature of the text, combined with a concise writing style and frequent use of abbreviations, makes it difficult to be processed through classical Natural Language Processing (NLP) pipelines. Due to these complexities, this text must be normalized before feeding to classical machine learning models. Developing these custom normalization pipelines requires manual labor and domain expertise and is a time-consuming process that demands constant updates. This leads to the under-utilization of this valuable source of information to generate insights to help with maintenance decision support. This study proposes a Technical Language Processing (TLP) pipeline for semantic search in industrial text using BERT (Bidirectional Encoder Representations), a transformer-based Large Language Model (LLM). The proposed pipeline can automatically process complex unstructured industrial text and does not require custom preprocessing. To adapt the BERT model for the target domain, three unsupervised domain fine-tuning techniques are compared to identify the best strategy for leveraging available tacit knowledge in industrial text. The proposed approach is validated on two industrial maintenance records from the mining and aviation domains. Semantic search results are analyzed from a quantitative and qualitative perspective. Analysis shows that TSDAE, a state-of-the-art unsupervised domain fine-tuning technique, can efficiently identify intricate patterns in the industrial text regardless of associated complexities. BERT model fine-tuned with TSDAE on industrial text achieved a precision of 0.94 and 0.97 for mining excavators and aviation maintenance records, respectively.

EFFICIENCY OF CMMS (COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM) PROJECT STAGES WITH K-MEANS

This study scrutinized four CMMS (Computerized Maintenance Management System) development projects using the K-Means clustering method to evaluate stages that require improvement by the GrX team. The projects under consideration were named Tim, Lix, Akb, and Mnk. The results indicated that the Tim project leaned more towards efficient CMMS development stages, while the Lix and Akb projects were predominantly in less efficient clusters at the implementation stages. The Mnk project had stages in less efficient clusters located at stages other than implementation processes. This suggests that each project has its unique challenges in achieving efficiency. However, the overarching conclusion is that the GRX team needs to enhance efficiency in the CMMS software development process. Several stages need to be evaluated, particularly the “data analysis and coordination” and “implementation of asset, work order, preventive and spare part modules” stages. Inefficiency occurs when the designing work time has a value lower than twice the workload. The K-Means algorithm was employed in the clustering process because it was believed that the data to be grouped was well-suited for implementation with this algorithm. The variables involved in this study were time and workload. This research provides valuable insights into the efficiency of CMMS development projects and offers a roadmap for future improvements.

Reliability Improvement in the Traction System of Tanzania Standard Gauge Railway

In recent years, many African governments have been developing Standard Gauge Railway (SGR) to replace the old after becoming absolute, inefficient Meter Gauge System (MGR) to carry more loads at high mileage and speed while combating the oil and environmental crises. With the increase of SGR, the amount of traction systems has grown sharply, the service life of the traction system has increased gradually, and the components of the vehicle traction system have become worn and aged as a result. While Tanzania is transitioning to SGR with its gradually increasing traction systems, reliability improvement becomes a key for the sustainability of its high-speed train services. This paper, therefore, aims to improve the reliability of traction motors used in Tanzania’s SGR. Firstly, the factors affecting the reliability of the traction system of Tanzania’s SGR and their significance are evaluated. Secondly, the computerized maintenance management system (CMMS) is designed to streamline and automate the maintenance management processes of the traction system of Tanzania’s SGR. Finally, a regression model is established to forecast the uptime based on the established factors. The obtained uptime hours were predicted in order to compute the reliability of the traction motor and its criticality in operation. Critical factors for improving reliability have also been highlighted. These findings play vital roles in establishing the fundamentals of factors that affect reliability as well as strategies and countermeasures to extend the operational life of the traction motors cost-effectively.

The Importance of Spare Strategy in a Research Facility

This document shows the importance of spare part strategy in a research facility at one of the most important medical schools in the world. It presents technical concepts related to equipment criticality analysis and spare part management integrated with computerized maintenance management systems (CMMS). A case study utilizing VFDs on campus will be produced to reveals gaps, benefits, and opportunities to improve the current spare part management system. Challenges in the spare part process will also be addressed in this document, such as storage and external vendor support.

Development of a methodology for asset integrity management in Universities and Research Centers

This article describes the application of a differentiated methodology that I developed to enhance and facilitate asset management work in renowned universities and research centers. Frequently, important scientific studies and research are interrupted or wasted due to unexpected failures in equipment and infrastructure systems provided by universities and research centers for researchers’ use. To address this issue, this methodology incorporates structured concepts and practices commonly employed in the field of asset integrity. These are uniquely integrated into comprehensive engineering assessment reports developed over several years during my fieldwork in various countries. This integration occurs within a computerized maintenance management system. The ultimate goal of this process is to maximize the reliability and performance of critical systems and equipment used in the infrastructure and conduct of highly relevant research at the national and international levels. Through case studies conducted at two major universities in the United States, the efficiency of this innovative methodology was analyzed quantitatively and qualitatively. As presented in this article, the main results obtained include a reduction in the number of unplanned repairs or shutdowns, improved quality and efficiency of maintenance programs, increased reliability and lifespan of critical research equipment and systems, an increase in the number of assets with an appropriate maintenance plan, and the enhancement of computerized maintenance management systems used by these institutions. These results translate into faster completion of scientific studies and research with more reliable data.

Current Overview of CMMS Operationality: Brazilian Scenario

Currently, the impacts of Industry 4.0 are observed in the construction industry, commonly referred to as Construction 4.0, emerging linked to technological innovations. Construction 4.0 presents a standardized model for smart cities and buildings, with the existence of some important points being essential, such as the capacity for adaptation, improvement and efficiency of resources and connections for everyone involved. As a result, new technologies and applications have been emerging that directly impact building maintenance activities, enabling increased efficiency and productivity in this sector, which reduces the risk of errors, failures and defects by maintenance managers. One of these applications is the use of computerized maintenance management systems (CMMS), which is a software solution designed to simplify building maintenance processes, in addition to improving the management of organizations' assets. In this sense, the present work aims to evaluate the CMMS available in the Brazilian scenario, showing the current panorama in which this technology has been presented to the market. To this end, we carried out a survey of the CMMS used by Brazilian maintenance companies using the snowball method to list the CMMS to be analyzed. The research carried out in 16 maintenance companies identified 9 CMMS, which were evaluated using the method proposed by Roscoff; Costella; Pilz (2020), which evaluates CMMS functionalities and activities. As a result, the sample analyzed presents heterogeneity in the results linked to CMMS functionalities and activities. In short, the registration items, a basic function, were met by all CMMS analyzed. However, advanced functions that are linked to the principles of Industry 4.0, such as interoperability, virtualization, real-time and service orientation, present disparities in results. Because among the nine CMMS evaluated, only three reached the levels proposed in the methodology.

Social innovation in working management practices to support facilities management at Malaysian polytechnics

Purpose Operation and maintenance (O&M) processes projects such as identification, assessment, planning and execution, embody a variety of standards such as technical (method of statement), environmental, economic (campus development) and social (health and wellbeing). Because these standards have proven to be challenging to integrate, local governments are increasingly experimenting with social innovation (SI) as a bottom-up form of standard integration. This study aims to apply the concept of SI to the O&M processes of facilities management at polytechnics in Malaysia to identify problems with conventional working practices in this area and to recommend potential solutions. Design/methodology/approach The paper reviews evidence that conventional working methods generate significant problems related to paper-based forms, improper database management and flawed decision-making processes. Because of the lack knowledge about different ways of how standard integration is achieved, the comparison of three polytechnic institutions which are Rensselaer Polytechnic Institute (RPI) and Southern Polytechnic College of Engineering and Engineering Technology (SPCEET) in USA as well as Seberang Perai Polytechnic, Pulau Pinang (PSP) in Malaysia shares the ambition to realise standard integration of O&M through SI. Findings The findings reveal that SI leads to four ways of standard integration: computerised maintenance management system, online customer complaint, electronic form and relational database. Application of the concept of SI reveals the need for more sophisticated management solutions in the O&M processes of facilities management. Originality/value These standard integration arrangements unfortunately seem to mainly contribute to greater alignment between standard rather than true standard integration. The concept of SI will guide future improvements and developments in maintenance management systems to fulfil requirements in this area.

To Improve The Efficiency and Productivity of Engineering Department by Implementing Computerized Maintenance Management System

To Improve The Efficiency and Productivity of Engineering Department by Implementing Computerized Maintenance Management System

SMARTS-Based Decision Support Model for CMMS Selection in Integrated Building Maintenance Management

An Integrated Maintenance System (IMS) represents a coordinated methodology including different maintenance policies, such as preventive and corrective. These systems rely on Computerized Maintenance Management Systems (CMMSs), specialized software available from multiple suppliers. Given the diverse features of commercial CMMS software, this work aims to develop a decision support model for CMMS evaluation emphasizing an integrated perspective within IMS. A Simple Multi-Attribute Rating Technique using Swings (SMARTS) method was used to build the decision model. Five existing market software were evaluated, and a minimum profile was defined for IMS requirements. Three of the assessed software types met these minimum IMS requirements, while the absence of certain features limited scores for others. The results obtained from the decision support model provide a simple and synthetic way to support decision-makers and promote a systemic view of the software features. The evaluation model has the advantage of adopting criteria that integrate software evaluation; its framing in a building maintenance management model; and new technological trends, such as Building information modeling (BIM), Virtual Reality (VR), Augmented Reality (AR), and Internet of Things (IoT). Considering these outcomes, future developments and alternatives can capitalize on these trends.

Detection and Diagnostic with Random Forest Classifier (RFC) to Improve the Maintenance Management System in Steam Boiler of Power Plant

Industrial internet of things (IIoT), digital twin, and connected devices can continue to use smart equipment and improve access to data. While the data collected by sensors has been an invaluable asset to companies, the ability to understand and use this data to drive new insights. The development of Condition Monitoring (CM) technology and Computerized Maintenance Management System (CMMS) in power generation systems provides a validated set of operation and maintenance data with abundant event data. Maintenance decision-making is primarily based on equipment reliability and performance-based features for diagnosing equipment failure. The most critical asset and often reduces the reliability and availability of a Coal Fired Steam Power Plant (CFSPP) with the most frequency of disturbances is the steam boiler. As a departure from the idea of creating a digital twin, this article will focus on analyzing equipment health conditions and finding causes of failure of the tools, utilizing data for diagnostic purposes. Real-case used in this research are steam boilers, which are important assets in power plant generation. The online and Failure Mode and Effect Analysis (FMEA) module data will be combined to realize the concept of anomaly diagnosis which is driven by hybrid data. Hoping that accurate diagnosis result with the Random Forest Classifier (RFC) Algorithm can be obtained and be used to analyze the causes of failure and decrease in equipment performance resulting by a decrease of energy efficiency performance. The analytical approaches are carried out to have the goal of generating detection models and diagnostic insights of event data based on operational data and FMEA.

Developing a framework for higher education institution building maintenance via soft system methodology

PurposeInadequate strategic planning and maintenance budget may undermine the maintenance of the Higher Education Institution Building (HEIB). Studies have shown that a customised maintenance concept such as Soft System Methodology (SSM) can improve public building maintenance operations. There is a paucity of studies regarding public HEIB maintenance in Nigeria via an SSM approach. Therefore, the research investigated the state of public HEIB and developed a framework to improve public HEIB maintenance practices in Nigeria.Design/methodology/approachThe research adopted SSM to understand Nigeria’s public HEIB maintenance practices. The SSM permitted a substitute approach to improve public HEIB maintenance practices via a developed framework. Data were collated via virtual interviews with experts, and findings were presented in line with the SSM seven steps.FindingsFindings show that besides the shoddy state of public HEIB maintenance, there is no public digitalised HEIB framework to improve maintenance practices across Nigeria’s higher education institutions. The study developed a digitalised framework with the support of Computerised Maintenance Management System from the findings. It would reposition the public HEIB and stir up various agencies/departments/units managing maintenance for better service delivery via integrated delivery, practical, methodological and managerial aspects.Originality/valueThe research investigated Nigeria’s public HEIB maintenance practices via SSM to identify the required document and propose a feasible framework to improve Nigeria’s HEIB maintenance practices. Besides the developed conceptual framework, Nigeria’s HEIB maintenance practitioners and higher institution chief executives can use the recommended framework as guidelines to improve HEIB maintenance practices.

Performance Evaluation of Faffa Food Share Company through Computerized Maintenance Management System (CMMS)

Since 1962 EC, the Faffa Food Share Company has been producing and supplying flour in Ethiopia both for relief and commercial markets. However, it is incompetent in the market due to a poor maintenance management system. So, the aim of this research is to overcome the poor maintenance management problems by using the CMMS application. The data collected from the company by survey studies, unstructured interviews, and stopwatches and from company records revealed that frequent failure machines, as well as a poor maintenance management system, cause production downtimes, resulting in a 29.19% decrease in production from the planned production. The collected data are analyzed by, tools such as time and motion study, SPSS software and CMMS effectively. The finding from the outcome of the bivariate correlation of CMMS elements and manufacturing performance dimension has an alpha α value of greater than 0.5, which indicates that the relationships between CMMS and manufacturing performance are positive. The findings from company records and stopwatch equipment and manpower (technicians) should be managed to activate machine servicing. Finally, the proposed smart maintenance management system tool, CMMS improves the availability of machines, reduces the frequency of equipment failure, reduces the breakdown time, decreases the cost of maintenance, and then improves the manufacturing performance by 5 percent.

FACILITY MANAGER’S ACCEPTANCE OF CMMS IN MALAYSIA: AN EXPLORATORY STUDY USING PLS-SEM APPROACH

Computerised Maintenance Management System (CMMS) is a widely recognised software in Facilities Management (FM) that provides numerous advantages to facility managers by enhancing productivity at work. The objective of this research is to provide insight and assess the factors influencing the acceptance of CMMS among facility managers in Malaysia. As a result, an improved comprehension of CMMS technology acceptance can be formed, as well as a better understanding of other FM technology adoption. Multiple factors influence CMMS acceptance, and this study used a quantitative technique to verify the hypothesis, using the Technology Acceptance Model (TAM) in conjunction with a Partial Least Square-Structural Equation Model (PLS-SEM). In addition, 125 facility managers completed an online survey that was utilised to collect primary data. The quantitative and structural models were analysed using PLS-SEM. Based on the study's findings, perceived usefulness (PU) and perceived ease of use (PEOU) have a favourable impact on behavioural intention (BI), which influences actual usage (AU) of CMMS. The research aims to improve awareness of the degree of acceptance of CMMS among facility managers and to contribute to FM organisations' better understanding of future technology adoptions.

Towards a technical sentiment lexicon for the maintenance of human-centred buildings

Following the “Cognitive Building” concept, in a few years, building automation systems were drastically improved to collect a large amount of user data. However, despite this evolution and the research efforts in the field, human-building interaction remained one of the least mature fields of building science due to the occupants’ complexity and diversity. Collecting data became simple and cheap, but transforming collected “data” into valuable “information” able to create an effective interaction between buildings and occupants remains complex. This work contributes by proposing a method to translate unstructured data, coming from Computerized Maintenance Management System (CMMS), into information useful to improve the interactions between occupants and buildings in the management of the maintenance process. End-users’ maintenance requests, collected through a CMMS, were used to create a technical sentiment lexicon able to predict the priority of an intervention based on an inverted naïve Bayes approach. Sentiment lexicons are part of sentiment analysis, an interesting research field introduced to study people’s opinions, sentiments, emotions, and attitudes through Natural Language Processing (NLP). The technical lexicon is useful to immediately perform the priority assessment of contemporary end-users’ maintenance requests, thus being more rapid than traditional Machine Learning methods.

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.

Building information modelling for airport facility management: the case of a US airport

Facility management (FM) at airports involves the use of systems such as computerised maintenance-management systems (CMMSs). Currently, the integration of building information modelling (BIM) with CMMSs is yet to reach maturity, resulting in the inability to reuse BIM data produced prior to the operation and maintenance phase. The lack of accurate equipment data and its untimely handover contribute to failed CMMS programmes. The studied US airport had a low CMMS utilisation, resulting from a lack of equipment data from recent and old construction projects, and it also lacked an efficient process for acquiring, storing and exchanging asset data from construction into its CMMS. To address this, the airport sought solutions to reuse BIM data in the CMMS. Utilising a design science research methodology, this research proposed and tested a solution technology innovation comprising a BIM–FM integration framework and a structured workflow based on BIM and Construction Operations Building Information Exchange (Cobie) for FM data delivery into a CMMS. The proposed solution was tested on a real-world project at the airport, showing efficacy in providing the required equipment data in a timely and accurate manner as compared with the existing practice. The study provides practical evidence of application of BIM and Cobie in FM data delivery from a handover perspective.