Developing a Novel Lean Methodology with Machine Learning Approach to Improve the Productivity and Efficiency of Rotating Pumps
Authors:   , Al-Toubi Soud Saleh 
Published: April 2024

Increasing the performance and effectiveness of maintenance processes is the main goal of production enterprises. The elimination of unexpected failures and the unavailability of spare parts, generate excessive costs and production losses. Maintenance is considered a main pillar of organisational performance. Notably, lean techniques can be incorporated into maintenance activities by applying their principles, tools, and practices, which are prerequisites for lean manufacturing systems. Lean maintenance is the continuous process of identifying and removing waste from maintenance operations, intended to gradually eliminate any factors that reduce asset reliability, increase costs, or negatively impact productivity. Owing to the rapidly changing business environment, organisations are forced to manage multiple challenges and complexities while systematically and continuously responding to changes related to sustainability. This PhD research was undertaken to evaluate the main maintenance practices and failures in Deep-Water Disposal (DWD) pump system by identifying and investigating events using the system’s historical data and developing innovative lean techniques to overcome these deficiencies, with the support of machine learning approach. Firstly, the 5S technique was used to address the spare parts handling issues that have a direct impact on maintenance activities. The Delphi approach was used to obtain expert judgement opinions through a series of questionnaires. The collected information was assessed by a selected panel of experts to consolidate their opinions and determine their weighted final judgement. The Gemba walk approach was also used to describe the observations of the executed tasks based on random cross-checks of the interactions between senior staff and their employees, to explore opportunities for continuous improvement. The results obtained indicate that employee commitment toward implementing the 5S methodology contributes significantly to improving the processing of spare parts in a warehouse. Furthermore, the analysis revealed that the efficiency associated with the spare parts logistics reached 90.1% in successive months. Secondly, an Artificial Neural Network with a Genetic Algorithm (ANN-GA) was implemented to investigate the Overall Equipment Effectiveness of the DWD pump system. The ANN-GA model was developed based on six big losses observed over eighteen successive months of operation and used to evaluate the current and future performance of the DWD system. Herein, 70% of the data was used for training, 15% was used for data validation, and 15% was used for testing. The DWD system encounters frequent failure issues, significantly impacting its performance. Thus, the main causes of these failures must be revealed and managed properly. The ANN-GA provides a linear trend prediction and assesses the confidence and accuracy of the results. One-wat analysis of variance was adopted as an additional tool for detecting the variability of the process parameters. ANN-GA results showed that the current Overall Equipment Effectiveness value ranges between 29% to 54%, whereas the average predicted system performance in the future is approximately 49%. This demonstrates the poor performance of the DWD pump system in the future compared with the standard target (85%). The ANN-GA analysis results were consistent with the measured values. Finally, the third technique focuses on diagnosing incipient failure early on by leveraging information from the vibration sensors. Failure of the DWD system leads to unexpected downtime, resulting in high operation and maintenance costs. In this context, a support vector machine (SVM) algorithm was combined with continuous wavelet transform for fault diagnosis of a centrifugal pump. SVM is generally formulated as a multiclass classification problem, and suitable features from vibration signals are devised as the inputs to the SVM classifier. The vibration signals were decomposed into different levels using the Daubechies wavelets, and statistical features were extracted from the detail coefficients and approximation coefficients of the wavelets. From the experimental results, the accuracy of the proposed method in detecting and diagnosing the main types of pump faults was approximately 84%. The present research helps to improve machine effectiveness by implementing novel lean maintenance techniques to control and eliminate the main causes of poor machine performance. Furthermore, effective lean maintenance involves proactive approaches that negate problems before it can fully manifest. Predictive and preventive maintenance strategy is recommended should be applied to minimise unexpected machine failures and production losses and identify the leading causes of failure. The outcome of study helps to establish optimal cost-effective servicing and replacement schedules for defective parts and prevent machine breakdown.