Analytic platform provides real-time view into equipment health
The continued functioning of integrated storm water management systems is crucial. These systems that channel, contain and convey storm water comprise significant environmental and public health and safety infrastructure. The U.S. EPA estimates that approximately 75,000 sanitary sewer overflows (SSO) occur annually in the U.S., resulting in the discharge of 3 to 10 billion gallons of untreated wastewater. The result: exposure to contaminated recreational waters from SSOs and CSOs contributes to up to 3,700 illnesses a year.
While these systems are critical, they are often not instrumented and are required to operate at peak performance at a moment’s notice. The risk associated with storm water management is growing as changes in atmospheric temperature are driving an increase in the volatility of the water cycle.
Intensifying Weather Events
This volatility is represented by intensification of weather events: The faster water cycles, the more abundant and more violent storms might be. This effect is manifesting itself in rainfall events that exceed common engineering design criteria. With the increasing intensity and frequency of wet weather events, “much of the existing and planned hydrologic infrastructure in the United States based on published rainfall design standards is and will continue to under-perform its intended reliability due to these rainfall changes.”
Moving From Time-Based to Condition-Based Assessment
Municipalities must adopt practices that maximize the operational availability of these systems while also maximizing capacity in the face of increasing intensity. In terms of storm water management, the National Association of Clean Water Agencies (NACWA) defines these Best Management Practices (BMPs) as “schedules of activities, prohibitions of practices, maintenance procedures, and other management practices to prevent or reduce the pollution of Waters of the United States.”
One of the most effective BMPs is to understand the current health status of the equipment employed in the delivery of storm water services. Because of the scale, volume and velocity of storm water events, many of the active control measures – pumps, control gates etc. – are large pieces of equipment. This makes them ideal for predictive condition assessment technologies.
While vibration analysis has been deployed for decades in some industries, the specialized nature of the equipment and the knowledge required to interpret the results has typically prevented widescale adoption. Advances in sensor technology, edge analytics and Artificial Intelligence (AI) have democratized the availability of these tools and has resulted in a dramatic downscaling of their application.
Advances combining rapid data collection and assimilation with the pattern recognition capability of AI means that equipment condition can now be assessed continually and at low cost. Departments no longer need specialized knowledge or extensive experience to make sense of the condition assessment data and to make actionable recommendations that reduce down-time, increase operational availability and save money.
VIE Technologies Inc. provides an AI-based fully autonomous predictive solution that uses vibration science at its core. The system includes a wireless sensor in an easily installed and long-lasting configuration that immediately starts delivering equipment-specific diagnostics. The sensor’s diagnostic specificity increases over time through continuous local learning of the machine’s behavior.
VIE’s analytics platform offers a real-time view into a machine’s health, identifying incipient faults, such as misalignment, imbalance, cavitation, bearing wear, winding issues, gear damage. These early insights allow time to intervene, thus avoiding failures that could result in expensive environmental contamination and a public health crisis.
VIE develops a machine-specific digital twin for each piece of equipment. Combined with highly reliable AI driven analytics, VIE can immediately assess the condition and ensure equipment is operating in its optimum state.
VIE’s prediction-as-a-service integrates vibration and temperature sensing with advanced machine learning to provide non-invasive and accurate assessments of the condition of rotating assets. The solution tracks the equipment’s behavior and develops an understanding of its operating environment to provide machine-specific prescriptions.
Recent results of prediction-as-a-service applications have enabled operators to:
- Extend intervals between service by at least 2 times, saving the facility 50% of labor costs and extending the life of the asset
- Identify the root cause of an undiagnosed, critical reliability issue, directed repair staff to the specific area of degradation and saved more than $100,000 in unnecessary repairs.
- Detect machine degradation sufficiently in advance of failure.
Expanding Capacity and Capability
To eliminate the public health hazard associated with storm water overflows, the U.S. EPA and cities are entering into multi-billion-dollar consent decrees for addressing the impact of wet weather and capacity limitations of these systems. Eleven municipalities have consent decrees that are in excess of $1 billion with obligations to build or replace significant wet-weather and sewer infrastructure.
A better use of those resources would be to maximize the capacity and capability of existing systems in the face of increasing frequency and intensity of wet weather events by providing clear, objective assessments of the condition of this infrastructure. Adopting practices that protect the equipment used in this endeavor is a cost-effective and quick means of achieving the goal.
•U.S. EPA, Addressing the Challenge Through INNOVATION, Office of Research and Development National Risk
•Management Research Laboratory
•R. Kerr, “The Greenhouse Is Making the Water-Poor Even Poorer”, SCIENCE VOL 336 27 APRIL 2012
•3 Wright, D. B., Bosma, C. D., & Lopez‐Cantu, T. (2019). U.S. hydrologic design standards insufficient due to large increases in frequency of rainfall extremes. Geophysical Research Letters, 46, 8144–8153. https://doi.org/10.1029/ 2019GL083235
•National Association of Clean Water Agencies, MS4 STORMWATER PERMITTING GUIDE, 2018