The utilities industry is the foundation of every other business and residence. When things are running smoothly, customers take benefits like power and water for granted. However, when something goes wrong, it disrupts daily routines and can bring commerce to a standstill.
Because utility providers have extensive infrastructure, there are many potential places for a breakdown in service. Predictive asset analytics is a tool that utilities can use to minimize service disruption. This process involves using historical data to create a predictive model for maintenance needs. Working with advanced algorithms, analytic software learns the operational profile of an asset and alerts the company at the first sign of an issue.
Using predictive models will change the way utility companies handle asset maintenance. Instead of a reactive model that waits for failure, predictive maintenance (PdM) addresses minor problems before they can cause serious disruption.
No one wants to be without their utilities. Sudden outages are frustrating and costly. Working with a predictive approach allows maintenance crews to make responsive service plans. In some cases, they can announce the upcoming disruption so that customers can prepare. In other cases, they can reconfigure the system to avoid shutdowns during maintenance.
In every industry, regular maintenance costs less than emergency repairs. Well-maintained equipment lasts longer and functions better. For utilities, this strategic care also avoids costs such as overtime for emergency work, lawsuits stemming from an extended outage and regulatory penalties.
A failing utility asset may pose a threat to the safety of maintenance crews and the general public. A proactive approach to maintenance gives crews more time to take appropriate safety measures. Weather data combined with predictive maintenance machine learning will also help personnel make targeted repairs ahead of a weather event.
Predictive maintenance (PdM) allows a utility company to send crews where they are most needed. The software assesses assets to determine their risk of failure. This measurement improves logistical planning so that maintenance personnel will be in the right locations to handle high-risk issues.
Asset analytics detects anomalies in an asset’s performance. While some unusual readings are a sign of a failing component, others can point to illegal draws on the utility. Crews can pinpoint the problem and stop the fraudulent use.
Adopting a new solution means changing the current system. In addition to changes in scheduling and logistics, implementing a predictive approach will mean adopting new procedures and taking on costs in other areas.
Predictive analytics solutions rely on historical data. Unfortunately, the initial information may not be well-organized. If a utility has been using several software solutions for asset management, critical data may be siloed. Before implementing an analytic solution, the organization may have to go through a process of data integration.
Legacy data can be another issue at the early stage. Data in older spreadsheets may have unclear labels. There might be gaps in data that will throw off calculations. An experienced development team can help a utility company choose appropriate data to produce the most accurate model. As the project continues, more measurements will increase the accuracy.
Cost can be an issue for any maintenance plan. As utility companies get started down this path, they may find that the initial maintenance cost will increase. Problems that once escaped notice are now demanding attention.
However, the long-term benefits of this process will outweigh initial costs. There will be fewer unexpected maintenance calls, a longer usable life for assets and fewer complications caused by outages.
In a reactive maintenance approach, the maintenance team uses the manufacturer’s specifications to determine an asset’s place in the repair schedule. However, the manufacturer may not have tested conditions that an asset may be subjected to in the field. Frequent exposure to extreme winds or other weather events may mean that a piece of equipment needs attention sooner.
In a predictive approach, a well-calculated model offers clues to an asset’s condition based on slight changes in performance. Catching small changes in behavior prevents a complete asset failure.
Creating models for various assets is a time-consuming process. Analysts must sift through historical data to find meaningful performance measurements. Then, they create a performance profile that sets normal operating parameters.
Testing is a fundamental piece of this process. In the early stages, an incomplete model may result in false alarms. The developers will dig further into the data in an attempt to refine the model. The result is a predictive model that is accurate and testable.
One of the benefits of working with a development partner is assistance with choosing the right technology for asset management. Several analytics platforms can sift through both structured and unstructured datasets.
To make an informed choice, the organization must consider the type and amount of data it will collect and analyze. It will also want to choose a platform that will easily integrate with its current software solutions for statistical analysis and modeling.
With careful development, predictive asset analytics will save a utility company time and money while improving its reputation with customers. With the right predictive models in place, a company can move from reactive maintenance that chases after emergencies to preventive maintenance that provides consistent operation.
For utility companies looking to move to a predictive maintenance model, Chetu has the knowledge and experience to deliver powerful solutions. Our development team has worked with many utilities to create effective management software. With an experienced development partner, utilities will reap the benefits of improved asset management, get in touch today.
Chetu, Inc. does not affect the opinion of this article. Any mention of specific names for software, companies or individuals does not constitute an endorsement from either party unless otherwise specified. All case studies and blogs are written with the full cooperation, knowledge and participation of the individuals mentioned. This blog should not be construed as legal advice.
Chetu was incorporated in 2000 and is headquartered in Florida. We deliver World-Class Software Development Solutions serving entrepreneurs to Fortune 500 clients. Our services include process and systems design, package implementation, custom development, business intelligence and reporting, systems integration, as well as testing, maintenance and support. Chetu's expertise spans across the entire IT spectrum.
- See more at: www.chetu.com/blogs