The Importance of Predictive Maintenance Anomaly Detection

Outdated and inefficient maintenance strategies can reduce machine productivity and lead to increased downtime. Unfortunately, planned and unplanned maintenance downtime can cause significant financial loss. Next-generation predictive maintenance anomaly detection can improve a company’s maintenance strategies and asset availability. 

Anomaly detection is central to predictive maintenance. Its primary goal is detecting anomalies in working equipment early on and alerting facility management to perform necessary maintenance. Predictive maintenance provides several benefits, including allowing companies to extend the useful life of their assets, avoid unplanned downtime, and reduce planned downtime. 

Today we will discuss why anomaly detection is a core function of predictive maintenance and emphasize the importance of predictive maintenance anomaly detection, allowing companies to better manage aging machinery and equipment and perform due diligence on assets before purchasing them. We will also introduce LLumin’s unique CMMS+ software as a leading condition-based predictive maintenance solution.

An Overview of Predictive Maintenance Anomaly Detection

Predictive analytics uses advanced data analysis techniques involving machine learning to identify anomalies and failure patterns that deviate from normal behavior. Predictive maintenance evaluates various factors that can affect the condition of a component or machine using historical data to determine if the data indicates a genuine problem.

The evaluation of historical data is necessary because it reveals whether an abnormality is caused by a degrading component (or machine) or is simply caused by a normal (seasonal) change in production demand.

The main goal of predictive maintenance is to detect anomalies and failure patterns, learn and study patterns that lead to machinery anomalies and faults, and anticipate mechanical issues before they occur. When an issue is identified, an alert or notification can be triggered and sent preemptively to facility maintenance technicians.

Benefits of Predictive Maintenance Anomaly Detection

Anomaly detection has paved the way for next-generation predictive maintenance. This technology evaluates large volumes of mechanical data using advanced data analytics and machine learning to determine the condition of a machine (or component) and predict how it will behave in the future.

The most important benefit of anomaly detection is that it can alert facility maintenance technicians when there’s an issue and save time, resources, and unnecessary maintenance costs. The process also helps determine why components fail in the first place. Several other important advantages of predictive maintenance anomaly detection include:

• Reducing planned downtime
• Avoiding unplanned downtime
• Optimizing maintenance resources
• Improving asset health and performance
• Optimizing production operations that increase customer satisfaction

We will discuss each below.

Reduce Planned Downtime

Anomaly detection identifies variables or items in a dataset that do not belong to an expected pattern and are usually invisible to the naked eye. Such anomalies, which we can refer to as early warning signs of failure, typically result in equipment failure or malfunction. Anomaly detection is used to trigger highly-efficient predictive maintenance tasks for faulty components. 

On the other hand, inefficient maintenance strategies commonly involve regular maintenance schedules that are followed whether or not they are required. Facility maintenance technicians may be required to work on machinery or equipment and handle problems based on limited data, signals, or alerts. These processes frequently lack a thorough root cause analysis and may involve replacing components early when these components could run for additional cycles until true levels of risk occur.

Avoiding Unplanned Downtime

Predictive maintenance anomaly detection, as an extension of the previously mentioned benefit, reduces unplanned downtime and increases equipment uptime. Because this technology enables companies to forecast asset and equipment failures from days to weeks in advance, manufacturers and operations personnel can schedule needed maintenance at opportune, proactive times to avoid unplanned mechanical failures.

Optimizing Maintenance Resources

Another advantage of predictive maintenance anomaly detection is that it can optimize the use of a company’s maintenance resources. Access to highly accurate data that predicts when a component or machine will fail allows companies to execute proactive maintenance schedules that reduce unnecessary maintenance, lower overall costs, and optimize resources.

Improving Asset Health and Performance

Equipment reliability can be measured by tracking various KPIs, such as mean time between failure (MTBF). Many manufacturers who use predictive maintenance notice significant increases in MTBF, indicating that their equipment is more reliable and likely to meet critical performance standards when using a predictive maintenance model. 

A better understanding of equipment performance in predictive maintenance enables businesses to keep machines running effectively to maximize MTBF, manage aging machinery better until it reaches the end of life, and conduct due diligence on expensive assets before purchasing them.

Improving Customer Satisfaction

Predictive maintenance anomaly detection involves the continuous analysis of performance data to identify signs of mechanical failure before it occurs, resulting in improved machine performance, reduced downtime, and lower maintenance costs. This leads to higher levels of production with fewer interruptions and, therefore, timely delivery of products into the marketplace. The technology can also assist field service technicians in setting proper expectations for the time and length of repair, improving field service productivity. These benefits frequently result in higher levels of customer satisfaction.

Implement the Best Predictive Maintenance Anomaly Detection Software With LLumin

One of the most significant challenges that manufacturers face is their inability to detect anomalies accurately in advance, resulting in an inability to handle mechanical issues before they lead to costly downtime. Our CMMS+ predictive maintenance software enables asset and maintenance management processes and best practices that effectively incorporate personnel, skills, materials and tooling resources, and supply chain partners. Using LLumin’s cloud-enabled CMMS+ solution, you and your team will have increased visibility of asset issues, statuses, and potential risks, all in real-time and at the right time.  
LLumin’s CMMS+ software aggregates data from machine sensors and applies condition-based workflows to execute immediate responses for optimal asset management. Your implementation will be configured to match your unique company goals and business processes. The CMMS+ software is easy to deploy, and your dedicated project manager will help you throughout the implementation process.

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Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track assets for industrial plants, municipalities, utilities, fleets, and facilities. If you’d like to learn more about predictive maintenance anomaly detection, we encourage you to schedule a free demo or contact the experts at LLumin to see how our CMMS+ software can help you reach your efficiency and cost-cutting goals.