Successful organizations understand that proactive maintenance is one of the most efficient and cost-effective maintenance strategies. Predictive maintenance (PdM), sometimes known as condition-based maintenance, is a proactive approach that checks the real-time condition and performance of business assets to determine if and when maintenance is necessary.
Predictive maintenance software uses data analysis tools and solutions to monitor and identify anomalies in business operations, allowing management to address possible defects before they lead to negative impacts.
What Is the Goal of Predictive Maintenance?
The primary goal of predictive maintenance is to find potential process bottlenecks or equipment malfunctions at the earliest opportunity to avoid more complex outcomes.
Predictive maintenance often uses sensors, the Internet of Things (IoT), artificial intelligence, machine learning, and data analytics to continuously monitor each asset's current condition and performance and detect possible defects to fix them before operational delays or failure occur.
When done properly, a predictive maintenance plan leads to multiple cost savings in repairs and replacement, higher productivity due to less downtime, and improved employee safety. Studies show that organizations implementing a predictive maintenance program save up to 30% on maintenance costs while significantly increasing their ROI.
Predictive VS Preventive Maintenance
Predictive maintenance is often confused with preventive maintenance, another proactive maintenance strategy. While they have the same goal of eliminating or minimizing the chances of equipment breakdowns, there are notable differences between each approach.
For starters, preventive maintenance is similar to scheduled maintenance because it relies on time-sensitive or usage-based triggers. For example, a manufacturing company can perform maintenance once its equipment has been used for a specific number of hours or after producing a fixed output.
However, since preventive maintenance happens on a set schedule, it can lead to "over-maintenance," which can cause equipment more harm than good.
On the other hand, predictive maintenance aims to cut back on redundant maintenance schedules by only fixing when necessary. Since PdM requires pin-point accuracy, it needs modern technologies to identify when equipment truly needs maintenance. As such, higher capital investment is necessary to use predictive maintenance for your business.
Top Technologies Integrated in Predictive Maintenance Software
Condition-Monitoring Devices
Companies use condition-monitoring devices, primarily sensors, to collect equipment information. There are different kinds of sensors depending on the information you need, from noise and temperature detectors to vibration and pressure alarms.
Artificial Intelligence and Machine Learning
Once sensors collect the relevant data, AI and machine learning systems analyze it to learn standard behaviors and eventually find anomalies in conditions or performance. AI and machine learning requires accurate and quality data to provide optimal equipment information.
Internet of Things
Businesses can leverage the IoT to convert sensor data into digital signals that can be analyzed over time, leading to accurate projections of when equipment maintenance might be needed.
The Benefits of Predictive Maintenance
Predictive maintenance has unique advantages over other types of maintenance, like preventive or reactive programs. Here are some of its benefits:
Access to Accurate Asset Data
Predictive maintenance provides managers access to real-time asset data, allowing them to create the most cost-effective plans to address equipment issues. The collected data can help management decide if specific machinery is better off replaced instead of receiving costly maintenance that may not keep it in long-term optimal condition.
Alternatively, managers can use CMMS (computerized maintenance management system) software to determine if maintenance costs exceed replacement costs to make a better-informed decision.
Fewer Equipment Failures (and Downtimes)
Maintenance managers try to avoid equipment failures at all costs due to the impact they can have on operations. Using condition-based monitoring helps managers take necessary actions before issues arise by getting real-time data from every piece of equipment. It also lessens downtime by reducing the mean time to repair (MTTR) by an average of 60%.
Better Asset Lifecycle Management
Since asset issues are detected and resolved before they can cause significant damages, PdM inadvertently improves asset lifespan. It also improves asset lifecycle management by simplifying repairs and adjustments. For example, a minor issue in an inexpensive part can sometimes lead to complexities in a more integral component, shortening the asset's lifecycle.
Process and Repairs Verification
Predictive maintenance sensors can perform vibration analysis, thermal imaging, equipment tracking, and oil analysis outside of day-to-day operations to increase operational security. Moreover, the sensors can double-check repairs to verify they are successful before reusing the machines. The secondary check-up helps reduces shutdown opportunities by identifying inadequate or incomplete repairs before operations restart.
Enhanced Workplace Safety
It's not uncommon to have equipment-related workplace accidents. In addition to being dangerous to employees, such instances can cause organizations significant financial impact in lawsuits and claims. Early detection of equipment issues can greatly reduce the risk of machine failures to eliminate workplace dangers and protect employees and the organization.
Improved ROI
PdM helps companies improve ROI by avoiding machine breakdowns without spending more on maintenance tools and services. Additionally, CMMS software helps maintenance managers increase productivity by allowing them to use more time on higher-priority tasks, as the software can easily go through data analytics in their stead.
Leveraging the Power of Predictive Maintenance Software
Predictive maintenance software is crucial for organizations looking to reduce maintenance frequency while ensuring their assets continue to be taken care of without exorbitant costs. Through sensor data, artificial intelligence, machine learning, and the IoT, managers can collect the necessary data to make better decisions regarding maintenance programs.
Like any other business strategy, predictive maintenance has its fair share of cons. It requires high start-up costs and specialized skills to run a successful program. However, the returns significantly outweigh the investment costs, especially if you factor in the time saved between maintenance sessions.
