The challenge in the industry
It is no secret that aging infrastructure presents a key challenge to the water industry. As these assets deteriorate, they pose serious risks to both water supply and water quality. In sewer system networks, the risk of blockages is a major public health concern and the impacts to the environment, the water quality and the assets themselves can prove costly both from a monetary perspective and reputational risk.
Imagine if a pump operator could identify and solve blockages in sewerage pumps before they even occur. This process would allow them to optimise maintenance cost by providing data driven insights to maintenance and engineering teams about what can be done before a blockage even occurs. Having the data available would enable operators to make informed decisions, reducing overall maintenance costs and increasing equipment availability to ensure business continuity.
We recently worked with a client that has approximately 800 pumping stations and each of these stations have varying quantities and ratings of pump drive trains, individual equipment from numerous manufacturers, and asset operation across vast geographical location with maintenance performed on a time – based schedule. To complicate this further, these assets are at varying stages of operational life, with equipment replacement planned based on operating time as opposed to asset condition.
The client was looking for a data driven condition based, predictive maintenance strategy across across pump station assets in their sewer system network.
Working in partnership with the client, the first sewerage pumping station identified had limited available data which was not unexpected due to the known age of the assets and subsequent data maturity. These data limitations meant that traditional analytics were not possible.
Overcoming the obstacles
An innovative approach was taken to develop a digital twin of the station by leveraging engineering and commissioning data. The digital twin was compared to the available operating data to validate the operating behaviour which provided confidence that the model could effectively soft-sense information not available from on-line instruments. Utilising subject matter expertise (SME’s), operating behaviours were identified and categorised based on their general impact on asset health.
Subsequently, a proof of concept (PoC) was designed to assess the asset health of the pumps across three of their sewerage stations by combining Advanced Data Analytics with motor and Variable Speed Drive (VSD) expertise with the aim of developing a predictive model to support a condition-based maintenance strategy.
The success factors of the PoC relate to the development of an algorithm and / or tools that will: detect abnormal operation of pump station drive trains (motor / pump / VSD); predict failure of the pump station drive trains.
Outcome of the PoC
Working in partnership as an extension of the client’s team, advanced data analytics were applied to propose a predictive model to support a condition-based maintenance strategy. However, in PoC studies such as this, it has further highlighted the need to focus on the digital maturity of the assets across the entire operations. Increasing the digital maturity would provide more informative data to be obtained. Increasing data maturity would enable clients to move from a predictive maintenance-based strategy to a prescriptive maintenance strategy. The power of this would ultimately ensure a calculation of lifetime left for the pump station drive trains possible, bringing a higher level of efficiency to asset management decision making.