Sewage systems hold a critical function within the fabric of urban infrastructure, primarily by ensuring efficient wastewater management. Within this network, sewage pumping stations emerge as important components. The performance of such stations is susceptible to being undermined by system failures and malfunctions, which can cause significant implications for both public health and occurrences of Combined Sewer Overflow (CSO) events. Consequently, discerning the patterns and root causes of these failures is of great importance.
The present study explores and seeks to understand the reliability of sewage pumping stations, particularly in the setting of the Department of Public Works in Rotterdam. The scope of this study is confined to data collected between 2016 and 2020. The research encompasses a diverse range of interconnected aspects, such as examining discrepancies in failure data, categorizing types of failures, tracking changes in failure patterns over time, and selecting a fitting statistical model to accurately represent the findings. The core focus of this study is to formulate an analysis framework to assess the reliability and failure patterns in wastewater pumping stations. In these patterns, various trends can be discerned through interarrival time - the period between two failures. For instance, an increasing trend indicates less frequent failures, suggesting improved reliability of the pumps. The devised framework combines both objective and subjective trend analyses, multiple trend tests, and employs a range of models such as the Homogeneous Poisson Process (HPP), Renewal Process (RP), and Non-Homogeneous Poisson Process (NHPP). This methodological approach is structured to facilitate a better understanding of the patterns of system behavior and shifts. Analyzing pump performance patterns over five years revealed various trends among 447 pumps studied. Of these, 254 had consistent failure patterns; 98 showed a stable trend, 146 improved over time with fewer failures, and 10 experienced more failures, indicating declining performance. The remainingpumps were divided into various trend groups or segments, demonstrating different patterns in their performance over the five years. The application of statistical methods elucidated these failure patterns, contributing to the evaluation of sewage pump station efficiency. Notwithstanding these progressions, my investigation has identified certain domains that could derive advantages from additional enhancements. The current methodology, which entailed manual configuration of kernel parameters and dependence on piecewise linear regression, was deemed insufficient for about 50% of the pumps. The study proposes the integration of sophisticated parameter estimation techniques, including Bayesian optimization or grid search, and alternative modeling methodologies to tackle this issue. It is recommended that forthcoming research expands its scope beyond Rotterdam and investigates a wider variety of pump mechanisms to validate the generalizability of the findings presented in this study. In conclusion, this thesis develops an analytical framework based on examination of failure patterns and system dynamics. It establishes a platform for future progress by outlining a distinct pathway for prospective investigations pertaining to the failures in sewage pumping stations.