Abstract
Maintenance has a significant impact on the business strategy of power organisations. Maintenance of power plants aims to promote safe, cost effective, and reliable electricity supplies. This kind of maintenance has complex activities and requires highly skilled maintenance staff; therefore, proper utilisation of technology is critical to achieve both plant safety and availability. The implementation of modern maintenance strategies has a positive impact on improving reliability and performance of power assets. Total Productive Maintenance (TPM) is an ideal approach to support the development and implementation of operation performance improvement.
Implementation of TPM programmes needs effective leadership and strategic planning skills. However, there has been almost no research applied in this area within Middle-East power plants. The power industry in the United Arab Emirates is in the early stages adapting proactive maintenance programmes, and the aim of this research is to thus investigate the critical factors that affect successful implementation of TPM programme in the Abu Dhabi power industry environment. The study is focused in the unique context of Abu Dhabi power organisations, and seeks to formulate critical success factors and enablers for overcoming barriers and obstacles to successful TPM implementation. To achieve the aim of this research, the researcher utilises a design of exploratory sequential mixed methods, starting with a qualitative approach. Semi-structured interviews were conducted with 16 employees, (including senior management, middle managers and senior engineers) to identify the technical and cultural barriers that could affect TPM implementation across power plants. The quantitative approach included a questionnaire which was distributed to 250 employees, comprising engineers, maintenance planners, technicians, and operators using a hand-delivered questionnaire. Structural equation modelling, using partial least squares path analysis (SmartPLS software version 3.0), was performed to test the hypothesised model. A sample of both approaches comprising three participant organisations was selected based on their experience in operations and maintenance of power plants, particularly in the power sector and included power generation, transmission as well as distribution organisations. In short, the empirical results confirmed the role of organisational culture and structural employee empowerment in participation of autonomous maintenance. Furthermore, this research is the first empirical study to examine the potential impact of organisational culture and structural empowerment on autonomous maintenance (operators) participation in maintenance activities in the context of the Abu Dhabi power industry.
A novel approach combining TPM with prognostic and RCM models has been developed. The researcher utilised a simulation approach (ReliaSoft’s Weibull simulation + Availability Workbench (AWB) Monte Carlo simulation) to appraise the impact of effective prognostic techniques and examine the role of reliability centred maintenance (RCM) on successful participation of autonomous maintenance (operators) in power plants using maintenance optimisation models based on artificial intelligence (AI) technologies. Historical data (degradation measurements + maintenance costs) from power transformers for critical power distribution substations were collated to conduct the simulation approach. The analysis is based on degradation analysis, and failure mode effective and criticality analysis (FMECA). Based on the analysis, remaining useful life (RUL) of critical power assets (transformer paper insulation) was determined and preventive maintenance (PM) intervals and costs of transformer tap changers (OLTCs) were optimised. Simulation results are presented to verify the analytical approach and validate proposed operational procedures. The proposed maintenance optimisation models performed good simulation results, and the latter show that the proposed models are really a new contribution in the area of TPM implementation in the power industry. Moreover, the integration of AI technologies with TPM implementation has enhanced the practice and theory of autonomous maintenance empowerment across power plants. The novelty of model is to build a proposed approach and solution based on operational steps and utilisation of a simulation model to empower and enable operator’s participation in maintenance activities in the power industry. With respect to the simulation results of power transformers, the findings can contribute towards improved equipment operations and successful implementation of TPM in power organisations. Furthermore, based on the study findings cumulatively, an innovative operational framework with detailed plans for TPM implementation was developed and formulated. This research has made several original contributions to knowledge in the parlance of maintenance management, with particular reference to TPM implementation across Abu Dhabi-based power industry organisations. The main contribution is the demonstration and establishment of a sound practical approach and model to integrate methods and principles of AI technologies within prognostic, RCM and TPM to support operators in decision making through identifying new policies for machine maintenance or assessment and enhancement of existing maintenance plans and costs. The model also delivers guidelines to operators for executing a practical, operational framework for TPM implementation.