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The purpose of this paper is to develop a systematic strategic approach to handle corrective maintenance onto the failures/breakdowns of construction equipment. For the maintenance crew/team, a breakdown code management is proposed, which will provide focused and unambiguous approach to manage any kind of breakdowns in construction equipments.

The past breakdown records of a construction organization in the UAE are considered for analysis. From the failure data, through cause effect analysis (CEA) tools, the components and the breakdown codes namely breakdown main codes (BMC) and breakdown sub-codes (BSC) are formulated. With Pareto analysis, the critical codes are identified and validated through failure modes and effects analyses (FMEA) tools for the critical effect on the affected components. From this identified BSC's further closer failure identification codes namely breakdown symptom codes (BSyC) and breakdown reason codes (BRC) are identified through fault tree analysis (FTA) tools. The approach to modified breakdown maintenance management (MB2M) with breakdown maintenance protocol (BMP) is envisaged.

The study was conducted on four different types of heavy lifting/earth moving/material handling system of equipment and further focused with two earth moving equipment namely dumpers and wheel loaders. Failure analysis is performed and the failure ratio and the component contribution to the failures are identified. Based on the information, the preliminary codes namely BMC and BSC are identified through CEA tools and the BMC and BSC are identified to find the most contributing codes to the maximum number of failures through Pareto analysis. Further the critical sub-codes are further verified through FMEA tools on the severity levels of the sub components due to these codes. The FTA methods are used to identify the closer reasoning and relations of these codes and the further codes namely BSyC and BRC are identified which are the exact cause of the failures. The management of breakdowns is further proposed through MB2M which includes BMP which provides all resources for the breakdowns.

The failure data collected are only pertaining to the Middle East region and applicable to similar regions for similar plant mix in construction companies. The sample equipment is only part representative of the construction equipment. A more robust model can be suggested in the future covering all aspects and for other regions as well.

The proposed methodology and model approach is highly adaptable to similar industries operating in the Middle East countries.

Many authors have studied the preventive maintenance models and procedures and proposals have been proposed. On the breakdown maintenance management of construction equipment, very few studies have been proposed mostly on the cost analysis. This model attempts to provide a code management solution to manage the unpredictable failures in construction equipment through failure data analysis on a construction organization.

This study defines service breakdowns, service breakdown prevention, and “servicide” as they relate to service-dominant logic. The study reviews relevant relevant literature on these three topics. This study categorizes real-life examples into five levels of dramatic turns toward service degradations and breakdowns that range from customer being aware but not mentioning service inadequacy to the service breakdown resulting in death of the customer or service provider. Taking initial steps in developing dramatic turn theory and improving the practice of service breakdown prevention are the major contributions of this study.

This study is a conceptual contribution that includes a dramatic turn role-playing exercise (at category 4 among five categories of dramatic turns for pedagogical/on-site enacting/practicing and training of service professionals. The study emphasizes and shows how to create and enact role-playing scenarios to increase requisite variety, provide training modules and increase skills/expertise in service enactment contexts.

Before explicit reviewing of the dramatic-turn performances, some of the participants as actors as well as audience members in role-play dramatic turns were quick to blame the customer behavior as the principal cause for the service breakdown. The study’s exposition stresses prevention of negative dramatic turns follows from experiencing and coaching a wide variety of customer and server interactions – achieving “richness” in enactments.

Research on service breakdown prevention needs to include field experiments on the efficacy of training programs for effective management of dramatic turns.

Training of service workers and service managers in experiencing/participating in dramatic turns is likely to be beneficial in reducing the severe adverse outcomes and unintended consequences of service breakdowns. Prevention, not only service failure recovery, needs to be an explicit focus in hospitality management training and assessment.

This study suggests tools and procedures to reduce the instances of the need for service breakdown recoveries.

The study calls attention and contributes a way forward in managing dramatic turns in hospitality service contexts. The study provides a nascent configurational theoretical foundation of dramatic-turn propositions. Given the severity of financial costs and loss of brand/firm reputation following the occurrence of extreme dramatic turns, a research focus on service breakdown prevention is necessary.

To study the breakdown (MI) mechanism in the sub‐micron MOSFET device.

Second‐order Poisson's differential equation is solved for suitable boundary condition to find the electric field expression for the sub‐micron devices. With the help of the electric field expression the exact relation for multiplication factor is derived, and then the equation for breakdown voltage has been generated.

This research paper provides the following findings: by controlling oxide thickness, junction depth and drain voltage, the breakdown can be easily controlled in the sub‐micron device; multiplication factor is not only affected by maximum field but also due to critical field; for very low gate voltage, the offset voltage mainly governs the breakdown; the breakdown voltage increases continuously as the channel length increases. It means, for larger channel length the breakdown will occur at high drain voltage.

This paper is based on the assumption that the electric field along the channel is independent of the junction depth (although not correct) and varying linearly from zero to E_sat.

The paper derived the exact expression of the multiplication factor. Also discusses that for MI mode of breakdown, the breakdown voltage increases slowly with the gate voltage and approximated by drain saturation voltage plus offset voltage.

Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However, uninterrupted utilization of plant and equipment is requisite to reaping the maximum benefit of mechanization; one particular problem being plant breakdown duration and its impact upon process productivity. Predicting the duration of plant downtime would enable plant managers to develop suitable contingency plans to reduce the impact of downtime. This paper presents a stochastic mathematical modelling methodology (more specifically, probability density function of random numbers) which predicts the probable magnitude of ‘the next’ breakdown, in terms of duration for tracked hydraulic excavators. A random sample of 33 machines was obtained from opencast mining contractors, containing 1070 observations of machine breakdown duration. Utilization of the random numbers technique will engender improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which aims to: extend the model's application to cover other industrial settings and plant items; to predict the time at which breakdown will occur (vis‐à‐vis the duration of breakdown); and apply the random numbers modelling to individual machine compartments.

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.

In high voltage direct current (HVDC), power cables heat is generated inside the conductor and the insulation during operation. A higher amount of the generated heat in comparison to the dissipated one, results in a possible thermal breakdown. The accumulation of space charges inside the insulation results in an electric field that contributes to the geometric electric field, which comes from the applied voltage. The total electric field decreases in the vicinity of the conductor, while it increases near the sheath, causing a possible change of the breakdown voltage.

Here, the thermal breakdown is studied, also incorporating the presence of space charges. For a developed electro-thermal HVDC cable model, at different temperatures, the breakdown voltage is computed through numerical simulations.

The simulation results show a dependence of the breakdown voltage on the temperature at the location of the sheath. The results also show only limited influence of the space charges on the breakdown voltage.

The study is restricted to one-dimensional problems, using radial symmetry of the cable, and does not include any aging or long-term effect of space charges. Such aging effect can locally increase the electric field, resulting in a reduced breakdown voltage.

A comparison of the breakdown voltage with and without space charges is novel. The chosen approach allows for the first time to assess the influence of space charges and field inversion on the thermal breakdown.

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web‐based data capture and information management system. Specifically, the system represents integration of a web‐enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.

The purpose of this paper to develop a novel strategic approach to handle corrective maintenance procedure in the event of a breakdown/disruption of service. A proposal to minimize the recovery time and the breakdown cost in the system in construction plant is presented.

The past plant breakdown records of a construction organization are considered for the analysis. From the previous breakdown records, a high level metric using Pareto analysis and the cause effect analysis is used to identify the main breakdown main codes (BMC) and the subsequent breakdown sub codes (BSC). Prioritized BMC and BSCs are used to formulate dedicated breakdown maintenance teams, which act swiftly in the event of the breakdown with the modified methods.

The study was conducted, on four different types of heavy lifting/earth moving/material handling system equipment, which are used to load/unload/haul and transport construction materials. Failure due to tyre puncture and allied problems contribute to maximum failure. A strategy plan to minimize this type of failure is proposed. With the identification of the most contributing BMCs and BSCs, it is further proposed to develop an “overall breakdown maintenance management”.

The collected data pertains to the construction plant located in a particular region, namely the Middle East, and hence the proposed solution is dedicated/relatively applicable to similar plant from the same region. A more robust model can be suggested considering the work environment in the other regions.

The proposed methodology is highly adaptable by similar industries operating in the Middle East region.

Construction plant and equipment contribute to the success of construction organizations, by providing enhanced output, reduced manpower requirement, ease of work and timely completion of the project. Delays in completion of projects generally have both social and economical impact on the contractors and the buyers. The proposed model will bring down the lead‐time of the project and enable the contractors to crash down their project completion time.

Numerous studies on preventive maintenance models and procedures are available for a system and in particular to construction plant maintenance in the literature. This model attempts to handle the issues of unpredictable breakdowns in the construction plant to minimise the breakdown time. The proposed model is a novel approach which enables a quick recovery of the construction plant, attributed from the breakdown parameters derived from the previous history of the work records/environment.

Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.