In the ongoing struggle to streamline maintenance processes and plan ahead for future work, predictive maintenance machine learning is changing the game by introducing intelligent automation.
For those in the energy sector, predictive maintenance is a fantastic way to save money and improve uptime and asset lifespan. But where does machine learning enter the picture? For context, predictive maintenance is used when you collect data about a particular piece of equipment to monitor its performance.
Looking ahead to future growth for the Predictive Maintenance sector, the data scientists at Market Research Future noted that the market is likely to grow by 25% CAGR, or $23 million, over the next few years.
In this blog, you’ll learn how machine learning is impacting predictive maintenance (for the better), how inverters are getting better uptime, and how your energy project benefits.
The Role of Machine Learning in Predictive Maintenance
On an immediate level, machine learning in predictive maintenance allows you to:
- Maximize the lifespan of your assets, and improve efficiency and uptime
- Integrate ongoing data collection into future decision-making about operations and maintenance
- Reduce downtimes, also reducing the likelihood of unhappy customers
If you’re in the energy industry, you already know the merits of Supervisory Controls and Data Acquisition (SCADA), a system that’s used to gather and examine data in real time. SCADA systems are used across plants in industries such as water and wastewater, transportation, and energy. Despite its commonplace use, SCADA does require human input when it comes to coding thresholds and configurations.
Though SCADA certainly has its benefits, machine learning for predictive maintenance more comprehensively integrates data from sensors, SCADA, and other sources, as well as contextual data like quality. Machine learning (ML) also includes data from machine collaboration, as well as the flow of energy.
Thanks to the intelligent responsiveness of ML, it’s far easier for you to get reports of anomalies and investigate correlations—all while machine learning continuously looks for patterns across multiple layers of data.
When you’re looking at implementing predictive maintenance models to improve your operations and maintenance workflow, you should consider:
- Error history: When establishing your machine learning model for predictive maintenance, you need to make sure your intelligent automation has access to data for both normal operations as well as failure events. You can collect failure data from your replacement parts records.
- Machine operating conditions: You also need to stream data from operating equipment as another source of information. As a machine continues to run, it’ll wear down over time. Your machine learning having access to this data over time provides another resource for upcoming predictive maintenance.
- Maintenance and repair history: With access to your maintenance and repair history, your machine learning algorithm gets a 360-degree view and provides accurate predictions and results.
- Static feature data: It’s also key to include technical information, such as when the equipment was made, the model of the system and where it’s located.
How Predictive Maintenance Models are Preventing Downtime & Predicting Inverter Failures
There are three key ways machine learning in predictive maintenance helps keep inverters from burning out and causing downtime:
- Regression models predicting Remaining Useful Life (RUL)
- Classification model to predict failure within a given timeframe
- Alerts for anomalies
Predicting RUL with Regression Models
By using static and historical data, you’re able to determine the time window you’ve got before a specific failure event takes place. This kind of prediction is also known as Remaining Useful Life (RUL).
Predicting Failure Within a Specific Time Frame
Though it remains a challenge to predict the lifetime of a machine, the good news is that you don’t need to do so. You just need to predict when your machine will fail next. With processed information like historical and static data, and with relevant event information, and the help of artificial intelligence for predictive maintenance, you can determine when your inverter will likely fail.
Providing Alerts for Anomalies
Flagging anomalous trends in failures is an ongoing process, and it really is a waiting game of collecting historic and static data to see what trends emerge. You’ll need to make sure data includes normal operational behavior from industrial or manufacturing processes, as well as allowing your machine learning to differentiate between regular operations and potential equipment failures.
Improving Inverter Performance With Predictive Maintenance
With the ability to reliably look ahead and optimize your maintenance schedule, you’re better able to account for the problems from your inverters, when they arise. With artificial intelligence for predictive maintenance, powered by machine learning, you’ll be able to:
- Reduce downtime of your energy project
- Increase profits and reduce operating costs
- Optimize your spending
- Improve personnel safety
- Extend the lifespan of your assets
- Save on energy usage
How a CMMS Remains Integral to Predictive Maintenance
CMMSes, like 60Hertz Energy, offer more than simple data aggregation. These software solutions can be deployed on the go, so you can collect and work with data no matter how remote your location. Multiple-language views also empower your team to communicate findings and collect data effectively.
Your CMMS also helps streamline your maintenance automation, eliminating manual labor and potential errors. It’s easy to automate and schedule maintenance work, as well as work orders so that your team has the information they need, when they need it.
Learn how a 60Hertz’s CMMS can benefit the maintenance of your energy project. Book a demo today.