IIC Journal of Innovation 6th Edition - Page 33

Outcomes, Insights, and Best Practices from IIC Testbeds: Intelligent Urban Water Supply Testbed equipment built from various manufacturers over a long span of time, interoperability is a big challenge and standardization is clearly important. Learning from this testbed in this area provides valuable input as WPG works in a number of standards in the water supply industrial vertical in China, as one of leading contributors. remote monitoring capability, for example, manual on-site inspections can be reduced with substantial savings in human resources. To be successful in analytics, enough data must be accumulated so that reliable models can be built to establish the operational norms from which anomalies can be detected, faults diagnosed and finally predicted. Root cause analysis and recommended repair procedures are also included as a part of the work flow. Predictive maintenance is still at a very early state because large amounts of data must be collected to enable reliable model building. The testbed is exploring building machine learning models to identify machine running states and anomalies. Because we cannot afford to wait for standards to be implemented and replace all of the equipment before implementing IIoT systems, it is essential to be able to adapt to the existing equipment that has already been installed. The testbed provides a flexible data adaptation layer to transform data so analytics can be performed in a normalized format. Progress has been made in monitoring equipment energy efficiency, currently on a pump-by-pump basis. The next step is to compare it to their peers’ normalized usage patterns when a sufficient number of pumps are connected. The goal is to increase energy savings by 30% from the current baseline. T ESTBED O UTCOMES From the use case perspective, the testbed plan is divided into 5 stages. After establishing the system and connectivity to the equipment, the first stage seeks to monitor the operational state of the equipment, then to identify anomalies and perform diagnostics. In the second stage, the testbed moves toward predictive maintenance. The third phase involves water quality monitoring and analytics. The fourth stage focuses on stored water quality improvement and balancing the water demand and supply. The fifth stage looks at the overall business model. The testbed has connected to a number of pumps equipped with water quality monitoring capability. It is able to monitor quality measurement and receive an alert if the quality is degraded. At this time, the testbed does not yet have instrumentation broad enough to enable model-building to infer water quality across a network. Based on feedback from the customers, the testbed has been working in a number of areas that are not explicitly outlined in the testbed proposal however are found to be valuable to the customers. These include features to: Across industry sectors, predictive maintenance has been considered the key IIoT application. However, visibility into the operation states of the remote equipment, e.g. if and how well they are working, already offers value to the operators. With - 32 - November 2017