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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.

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.

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.

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.

The construction industry relies increasingly on profits generated from high utilisation of mechanisation. Interruption of this mechanical supply not only incurs the “tangible” costs of labour, replacement parts and consumables, but also the less tangible costs of delays to contract, possible loss of client goodwill and ultimately, loss of profit. Cumulative costs associated with plant breakdown are therefore significant. Predictive maintenance (PM) techniques have evolved to keep a check on mechanical health, by generating information on machine condition. Such data allow just in time maintenance to be conducted. However, recent developments have witnessed an increased interest in determining “root cause” of failure as opposed to monitoring the time to breakdown once the wear process has begun. This paper reviews condition based monitoring (CBM) technologies and introduces the evolving concept of root cause analysis. Both these could have particular relevance to construction plant and equipment. In summary, the paper presents initial findings of ongoing research, which is the development of a model for predicting construction plant and equipment breakdown.

AT THIS PERIOD of British Industrial history, executives from the highest echelons of management down to the ordinary worker on the shop floor must be wondering what the future has in store for them. What with takeovers and Government sell‐outs the position of anybody can no longer be regarded as safe.

Alec Snobel reports on the growth of a specialist cleaning and maintenance industry.

“A flying squad of industrial detectives” was how the local press described the Industrial Unit's engineers, on the occasion of the Duke of Edinburgh's visit to the Unit after the Tribo‐International 78 exhibition.

Petroleum products are prime commercial sources of energy throughout the world in spite of the impressive efforts of the International Energy Agency (IEA) and European Economic Community (EEC) to find viable alternatives. Energy is the vehicle for economic development and the policy of the Nigerian government is that petroleum should be tapped, developed and optimally distributed for the overall development of society. Owing to several factors, the distribution and marketing of petroleum products have developed hydra‐headed problems constituting a major source of concern and embarrassment to the government, private organisations and individuals.

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.