Editorials
How Predictive Maintenance Technology Optimizes Machine Monitoring
POSTED 01/28/2025 | By: Juan Carlos Carcia, Product Manager - Components
Predictive vs. Traditional Maintenance
There are three different types of common maintenance methods. Reactive maintenance is a strategy that involves addressing equipment failures only after they occur. This means that when something breaks down, it must be fixed or replaced, often leading to unexpected downtime and halting the process until the issue is resolved.
Preventative maintenance is a time-based approach where machines are inspected manually at regular intervals, such as weekly, monthly, or annually. This method is time-consuming and resource-intensive, requiring either in-house staff or external contractors to perform the inspections. It does not provide real-time monitoring, meaning issues can arise between inspections without being detected.
Predictive maintenance is a proactive, condition-based strategy that uses IoT sensors to monitor equipment in real time. These sensors send data to your control system, office, or even your smart device, allowing you to check the condition of the equipment at any time. Unlike traditional methods that rely on reactive measures or scheduled checks, predictive maintenance focuses on early detection of potential issues. This reduces downtime and maintenance costs while streamlining operations by eliminating the need for manual and time-consuming data collection processes.
Costly Disadvantages with Manual Methods
The cost disadvantages of these traditional maintenance methods, such as reactive and preventative, in manufacturing are significant, particularly when unexpected machine failures and production halts occur. These failures result in substantial financial losses due to downtime, as production comes to a standstill, leading to lost revenue. The costs to repair or replace the machine can also be high, especially if specialized parts or external expertise are required.
The shortage of skilled labor further complicates the situation, as finding the necessary experts is both time-consuming and expensive. This delay in securing skilled workers can lead to extended periods of non-production, exacerbating financial strain. Additionally, the cost of outsourcing these resources can be substantial, adding to the overall financial burden.
Benefits of Predictive Maintenance
Predictive maintenance addresses the issue of relying on individual worker knowledge by using data and trends from continuous monitoring. Existing methods often depend on the expertise and intuition of workers, which can be inconsistent and not easily transferrable. In contrast, predictive maintenance systems collect and analyze real-time data from sensors or devices, allowing for the identification of potential issues before they lead to failures.
By leveraging data, predictive maintenance ensures that maintenance activities are optimized and performed only, when necessary, based on the actual condition of the equipment. This data-driven approach makes the process more reliable and consistent, as the information is available for everyone to read and act upon, regardless of individual expertise.
Data Collection and Monitoring
Any parameter useful for monitoring a machine can be collected through predictive maintenance systems, but a worker can gather this data manually as well, given the time and availability. For example, with a motor, you can collect vibration, current, insulation resistance, temperature, torque, and harmonics, among other parameters. However, predictive maintenance automates this process, providing real-time data and significantly reducing the time required for manual readings, thus enhancing overall efficiency and effectiveness.
Connecting predictive maintenance devices to control systems is essential for enabling remote real-time monitoring of machinery. This connection facilitates the continuous transmission of data to a remote location, where it can be visualized and analyzed. As a result, maintenance teams can track the condition of machinery without the need for frequent on-site inspections, which reduces maintenance costs.
Real-time monitoring optimizes the use of skilled labor by minimizing the time spent on manual data collection and inspections. Instead, skilled workers can focus on more critical tasks. Furthermore, this setup ensures more efficient and timely maintenance activities by predicting when equipment needs maintenance, allowing for interventions only when necessary. This proactive approach helps prevent costly breakdowns and unnecessary downtime, ultimately leading to more reliable and efficient operations.
Advanced Algorithms to Predict Trends
The system must have an internal algorithm to predict failures based on trends. It continuously learns while operating, differentiating normal scenarios from critical ones. By analyzing measurements, the algorithm identifies whether conditions are normal or abnormal. This predictive capability helps in separating these scenarios and sending relevant information to control systems like SCADA. The algorithm also adapts to environmental impacts, enhancing its predictive accuracy over time.
The idea behind predictive maintenance is that it's not difficult to implement into a machine; you don't need a skilled worker or skilled labor resources to implement this strategy. The device does most of the work, so it's as simple as plugging it in, and it will start working. The main task is interpreting the information it provides, telling you what's happening and what actions to take.
Conclusion
Predictive maintenance technology significantly enhances operational efficiency and reliability by automating real-time data collection and analysis, which minimizes the need for manual inspections and skilled labor. By enabling early detection of potential issues, it ensures timely interventions, preventing costly breakdowns and unnecessary downtime. This proactive approach optimizes maintenance activities, leading to more efficient and dependable operations.