November 5, 2021
A predictive maintenance solution analyzes sensor data and predicts equipment or vehicle failure by consistently monitoring equipment conditions. This advanced warning allows you to take proactive, corrective action. Find out why predictive maintenance results in less downtime, reduced costs, and extends the lifespan of your assets.
IIoT is a transformative technology, fundamentally altering how companies operate. In particular, predictive maintenance is paving the way towards IIoT adoption, accounting for the largest share of the market. And it’s no secret why businesses are snapping it up: Companies attribute predictive maintenance with massively reducing unplanned downtime, reducing costs, and extending the lifespan of their equipment and vehicles.
Predictive maintenance (sometimes shortened to PdM) is a data-driven, proactive technique used to monitor the condition of vehicles and equipment to detect abnormalities in operations. This prediction enables businesses to fix things early, preventing costly reactive maintenance.
For example, the manufacturing industry has adopted predictive maintenance for quick wins. In collecting data from their factory and equipment sensors, they can analyze it and discover warning signs of costly failures before they happen, maximizing uptime.
Automotive companies have also implemented predictive maintenance programs to benefit consumers. Vehicle sensor data gets relayed back to manufacturers or dealerships, who can alert drivers of upcoming issues. In turn, customers can schedule serving before a breakdown. Similarly, utility companies use predictive maintenance to improve service by implementing smart meters that detect when there are supply and demand issues on their grid. They can fix these issues before an outage, preventing expensive emergency repairs and avoiding angry customers.
Predictive maintenance bridges the gap between human intuition and preventing equipment failure. By using real-time data, you don’t just prevent failure; you optimize maintenance and workflows.
Asset management with predictive maintenance often begins by being condition-based, meaning that if certain predefined operating conditions or parameters are met, you should get the equipment serviced. The conditions and sensor data are managed, analyzed, and compared through computerized maintenance management systems (CMMS). Over time, as your CMMS accumulates more historical data and builds more accurate models, its recommendations become more accurate.
At a basic level, predictive maintenance is made up of three steps:
Capturing data. Equipment sensors generate data.
Transmitting data. The captured data is shared with maintenance software via a wired or wireless internet connection.
Analyzing data. Software algorithms identify trends and compare a piece of equipment’s performance against expected performance (often based on OEM guidelines or historical data.) It also factors in natural deterioration. Once computed, the software can forecast when equipment and vehicles will need maintenance.
Ultimately, in following these steps, companies can predict when equipment failure will occur and prevent failure by scheduling regular and corrective maintenance.
You may hear people interchange these two terms, but they’ve got distinct differences. What makes matters even more confusing is that businesses use both predictive and preventative maintenance strategies in the same program.
Preventative maintenance is based on usage or time triggers. It involved inspecting and performing maintenance regardless of the asset’s needs. An example of a preventive maintenance program is getting your car’s oil changed. Generally, car owners get their oil changed every three months or 3,000 miles, whichever comes first. This rule satisfies both the time and usage conditions. However, preventative maintenance doesn’t require condition monitoring, which predictive maintenance relies on.
Predictive maintenance is much more precise, based on sensor data. It uses preset conditions and uses different technologies, including artificial intelligence (AI) and machine learning. Going back to the oil change example, what if your car could perform oil analysis? Say your car sensors could constantly evaluate engine lubrication conditions and oil contaminants. It would relay that data to maintenance software. At some point, the car’s oil will reach a certain viscosity or contamination level (a predetermined condition) The software would trigger an alert, notifying you to get your oil changed within two months based on your driving history and state of oil. This a simplistic version of predictive maintenance, but you can see how it relies on condition monitoring to give you well-defined predictions.
While predictive maintenance requires a larger investment to get up and running than preventive maintenance, overall, there are greater time savings and reduced maintenance costs.
These predictive maintenance benefits are major factors in contributing to IIoT’s growth.
When investing in predictive maintenance technology, you’re investing in the long-term health of your equipment or fleet. Because CMMS is constantly monitoring asset performance, you’re preventing real equipment damage from occurring. You can rely on equipment conditions and gain peace of mind to focus on other pressing tasks.
By increasing an asset’s lifespan, you’re doing several things to reduce operational costs and save money. First, predictive maintenance helps maximize your equipment’s return on investment. While it may be more costly to implement upfront, it saves you money in the long run by keeping your critical assets in good working order, longer.
Second, because you’re only servicing and spending on necessary repairs and upkeep, your maintenance shop’s bills are going to go down. A PdM program helps maintenance teams plan ahead. They don’t need to keep spare parts on hand or pay for expedited shipping of replacement parts.
Furthermore, you’ll collect valuable data on which types of vehicles or equipment are performing. With this data, you’ll be able to make wise investments in future assets, making a predictive maintenance program a cost-effective business tool.
Preventative maintenance software ensures needed maintenance tasks are completed, decreasing the number of unplanned repairs. Using advanced analytics to predict asset failures, can increase equipment uptime by up to 20%. This means your vehicles are on the road and experiencing less downtime. Equipment stays online throughout the full manufacturing lifecycle without a hitch.
According to Deloitte, poor maintenance strategies can reduce the overall productive capacity by 5-20%. Implementing a reliable PdM process with regular maintenance activities boosts productivity and efficiency. Since the need for service is known before it’s required, maintenance tasks can be scheduled when equipment is available. Drivers are less likely to get derailed by unexpected breakdowns. Managers don’t have to drop everything to find a solution. Maintenance teams aren’t surprised by extra work. Having regular maintenance schedules keeps the operations chain happier and running efficiently.
It’s also better for the environment. Sensors monitoring equipment like refrigerators and air conditioners can detect greenhouse gas emission leaks faster than traditional methods. Predictive maintenance software can detect potential asset failure before releasing harmful emissions, or worse, dangerous, toxic materials.
While there’s no clear-cut answer to this question, it’s agreed that both predictive and preventative maintenance is preferable to reactive maintenance. The solution that’s better for you largely depends on your business’ needs. When figuring out the maintenance techniques, you‘ll want to consider the following.
Cost savings. Both offer cost savings. Predictive maintenance requires a larger upfront investment because there are incurred training and maintenance tool costs. However, the rise of cloud-based technology has helped to decrease costs.
Time savings. Compared with preventive maintenance, predictive maintenance ensures a piece of equipment or vehicle only needs maintenance right before impending failure. This reduces the total downtime of the asset.
Condition-based. Predictive maintenance relies on sensor data instead of time-based usage. This is more precise, ensuring that you’re not pre-emptively using resources to service something that doesn’t need it yet.
Combined solution. Maybe the technique that works for you is a combined solution. You might use predictive maintenance on critical assets while opting for a simpler usage- or time-based method for lower priority equipment. There are many use cases where predicting a failure is not cost-effective—for example, a legacy truck in your fleet that you’ll replace soon.
Samsara predictive maintenance software is an integrated, user-friendly platform for equipment and fleet management. We provide solutions for equipment monitoring, GPS tracking, dash cams, site visibility, mobile apps, and more.
With Samsara, you can:
Easily establish baseline measurements, create a predictive maintenance schedule, and assign maintenance tasks with alerts.
Use sensor data to calculate key performance indicators based on vibration analysis, temperature, power consumption, and output.
Monitor conditions in real time to identify issues early.
Avoid unplanned outages to reduce downtime.
If you’re looking to extend the life of your equipment and vehicles, reduce maintenance costs, and enhance efficiency, we invite you to try out Samsara’s predictive maintenance solution. Sign up for your free trial.
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