Track asset performance using real-time health scores
As we learned in last week’s post from Kansas City BPU, providing clean, safe water to a large community is a cumbersome task. This post will dive into how one regional water supplier tracks asset performance using real-time health scores. Using data from IoT sensors, they can produce a sustainable, reliable water supply for their community, and improve the lifespan of their most critical assets.
A complex system makes performance monitoring difficult
This supplier owns 174 wells across fifteen well-fields, eleven treatment facilities, eight pumping stations, 240 miles of large diameter pipe, and a large reservoir storage. The reservoir storage is critical during the driest months to maintain supply to customers with limited or no interruption. It is a complex system to manage and optimize. It is important to have a solid grasp of the assets that power their wells, facilities, and stations, as well as the performance and health of these assets.
Today’s assessments are condition-based
Like many organizations, asset assessments are done based on condition. Every 3-8 years, the assets are visited and a formal assessment is performed. Using an in-house designed program, one questionnaire per asset type is produced, and this, in combination with photos, is how the status is documented and performance monitored. These assessments are stored in a database and fed into the system that determines when, and if, an asset will be renewed or replaced.
For a safety pump, for example, the types of questions on the questionnaire may include (among others):
- Are all safety guards present?
- Is there excessive noise?
- Is there excessive vibration?
- Are there any leaks?
- Is the pump missing any components?
- Is there unusual smell or heat?
- Does it meet capacity needs?
- Is there any corrosion?
Using the data collected, the system can utilize condition information, along with baseline expected asset life and failure curves, to project capital costs that will be needed in the future.
Moving to Maximo & real-time health scores
The current model is fairly effective but it is neither agile nor predictive. Inspections are years apart, pumps or pipes can go down because maintenance is not performed quickly enough to avoid failure, and recommendations for capital investments are not based on real-time data. The information collected could be months old when it is used to make decisions.
Using real-time condition monitoring by placing IoT sensors on all assets, this supplier monitors data points such as vibration, temperature, battery level and run-time. This data is then input into Maximo. Maximo is the world’s leading enterprise asset management solution, powering nearly every asset-intensive industry in the world. By supplementing their current condition assessments with this sensor data, they perform mini-assessments on a month-to-month basis, rather than every 3-8 years. They can also calculate the remaining useful life of the asset using Maximo. This shift provides them the ability to make better decisions about weekly workload prioritization and capital expenditures. It also helps in reducing asset failures.
From prototype to Maximo Asset Health Insights (MAHI)
Using the IoT for preventive maintenance, you can improve asset maintenance and reduce the potential for failures. MAHI, IBM’s asset health scoring tool, does that for this water supplier. They piloted 45 assets in MAHI, including a mixture of pumps, generators, and motors across multiple sites. They standardized the condition assessment questionnaires, collected meter readings more frequently, incorporated mini-assessments into worker job plans, and determined where data feeds could replace subjective questions. After completing a successful pilot program, this supplier is now eager to expand the program to include an additional 2400 assets.
Learn more about preventive maintenance and MAHI
Read this Aberdeen report on why best-in-class firms are maintaining their most critical assets with EAM & IoT.
Take the first steps towards understanding IoT for Preventative Maintenance.
References and images for this use case and Kansas City BPU used by permission: MaximoWorld by Reliabilityweb.com.