Predictive Maintenance: Using Monitoring Data to Prevent Equipment Failures
Every facility must keep its equipment running efficiently to ensure productivity. Without a maintenance strategy, facilities will experience breakdowns and asset downtimes that will disrupt operations and damage their reputation. With advances in communications and monitoring technologies, predictive maintenance is growing as a standard practice in many industries.
Understanding Predictive Maintenance
A predictive approach to maintenance harnesses the emerging power of machine learning combined with the Internet of Things. Facility managers can gather streams of real-time performance data by attaching networked sensors to equipment. As this data pool grows, machine learning resources analyze it, looking for patterns and deviations from the norm. The primary advantage of this maintenance technique is allowing managers to see potential problems well before they lead to downtime.
Other Common Maintenance Techniques
Facilities have adopted a variety of approaches to system maintenance over the years. With every strategy, they seek to strike a balance between avoiding breakdowns and performing unnecessary maintenance tasks.
The most straightforward approach to asset maintenance is waiting for failure. This strategy works best for equipment that is inexpensive to replace or simple to repair. However, a reactive approach with more complex machines can leave a facility without essential equipment. Reactive maintenance is also stressful for repair crews performing frequent emergency repairs.
A preventive maintenance strategy is also known as scheduled maintenance. Following manufacturer recommendations, maintenance crews perform tasks on a predetermined schedule. This approach has the advantage of predictability. However, it often leads to extra expenses when equipment receives servicing it does not need.
Benefits of Employing Predictive Maintenance Insights
Predictive or conditional maintenance allows for a more responsive approach to asset care. Managers assign tasks based on performance indicators that point to a machine’s condition. This strategy brings several benefits to facilities.
Less Frequent Downtime
The point of a predictive model is to catch issues before they shut down a machine. Crews can schedule tasks at a time when it is least disruptive to facility operations. Repairs happen when sensor data indicate changes in performance rather than a complete breakdown.
Networked sensors constantly send performance data for analysis. Predictive maintenance analytics allow personnel not only to prevent shutdowns but also to optimize performance. Equipment will run at ideal settings, making its operation more predictable.
Longer Asset Life
Equipment that receives adequate care has a longer usable lifespan. Facilities will garner a better return on their initial investment by letting equipment run longer before replacement.
Lower Maintenance Costs
It costs less to run a predictable maintenance strategy. Using predictive maintenance techniques helps facilities make informed decisions about inventory and personnel needs. This approach also avoids the expense of having crews work overtime to handle emergency repairs.
Types of Conditional Maintenance Measurements
Sensors can send information about many different performance markers. Facilities must decide what information is most likely to yield relevant insights.
Unexpected temperature rises often show that a component is working harder than usual. This change may indicate that it is approaching the end of its usable life. For electronics, a lack of heat may demonstrate a dead part.
Unusual shaking is a sign that a machine is not operating correctly. However, sensors can detect deviations from normal vibration levels much earlier.
Measuring factors like current and resistance helps pinpoint failed electronic components. Repair teams can replace a single part rather than deal with a cascading failure.
Pressure readings are critical in assessing a pump’s health in water facilities. Since pumps tend to lose efficiency over time, employees can address performance issues before they impact the facility.
Flow rate is another indicator of asset health in the water industry. Changes in water flow may indicate a clogged filter or another problem that requires attention.
SCADA and Predictive Maintenance Analytics
System Control and Data Acquisition technology fits naturally with a predictive maintenance strategy. SCADA systems have generated data for industrial facilities for decades, giving personnel access to real-time performance information. However, the advent of machine learning and artificial intelligence resources has empowered faster and deeper data analysis, leading to predictive insights.
Choosing a Cloud-Based SCADA Model
High Tide Technologies provides SCADA solutions for municipal water facilities that can empower a predictive maintenance strategy. We use a cloud-based system, giving data access to any device connected to the network at any location. This arrangement is ideal for water treatment, collection, and distribution systems that cover a large geographic area. Contact our team today to learn how our SCADA technology can enhance the maintenance of your system.