What are the different predictive maintenance tools ?
Make maintenance smarter and eliminate unplanned downtime with predictive maintenance tools.
$50 billion a year is the overall estimated cost of unplanned downtime for industrial manufacturers. 82% of companies have experienced at least one unplanned downtime incident over the past three years. Most have experienced even more than one.
This is caused by a not optimal maintenance management often based on a reactive approach to maintenance.
Unplanned downtime often lasts a long time, an average manufacturer deals with 800 hours of downtime per year, and can amount to thousands of dollars per minute. Industries should try to avoid these unscheduled downtimes at all costs.
Fortunately, thanks to Industry 4.0, the digital transformation that is taking place in the industrial world, it is possible to reduce this unplanned equipment downtime by implementing a predictive maintenance (PdM) strategy.
These downtimes are not negligible. If you have been lucky enough to be spared until now, investing in a system will allow you to save money very quickly.
predictive maintenance tools
Predictive maintenance is a proactive, data-driven approach to maintenance. Predictive maintenance software uses data collection and analysis to estimate when equipment is likely to fail, so that corrective maintenance can be scheduled before the failure occurs.
The goal is to schedule interventions at the most appropriate and cost-effective time. As a result, industries would increase equipment lifespan and reduce unplanned downtime.
Condition based monitoring systems are the most common systems to evaluate assets’ performance in predictive maintenance. They are usually based on IoT sensors that record data in real time to evaluate the evolution and changes in the condition of the equipment over time. (while they keep running). The PdM systems therefore indicate when a failure is likely, and therefore when equipment maintenance will be required and how much time is left before the failure.
What is the Best condition monitoring system?
Vibration monitoring and analysis of your machinery can determine machine health
Also named infrared thermography, it checks abnormal temperature changes that could be failure related
Motor current signature analysis
Provide machine health diagnosis by analyzing the electical signature and observing variations from normal patterns.
Lubricant analysis can give you information about the machines’ condition.
Most problems emit high frequency sounds. By analyzing them, we can identify existing or developing failures
Each system has different advantages and disadvantages. We have made a comparison to help you choose which one is the best for you!
Predictive maintenance Technologies
Health monitoring sensors use two main technologies:
- Wireless sensors that measure and gather operations and equipment real-time data. Sensors are installed on the machine to collect raw data. By using the Internet of Things (IoT) technology, data is collected 24/7 and is available in real-time. With some systems, the data is reorganized to allow the machinery data visualization on an online platform.
- Algorithms using data science and machine learning to identify equipment failures and vulnerabilities. Data collected by sensors are analyzed by predictive algorithms that enable evaluation of the asset’s efficiency. The algorithms identify trends and patterns to detect when a failure is likely to occur and when an asset will require servicing or replacement. The actual behavior of the equipment is then compared to the expected behavior that it should have when operating properly. The variations allow us to identify which problems will occur and at what level of degradation they are.
Why is predictive maintenance important?
Predictive maintenance diagnostics are extremely valuable as they optimize the maintenance plan and increase the reliability of the production. Predictive maintenance techniques help industries to
- Avoid unexpected breakdowns and costly unplanned downtime
- Reduce operational costs by performing scheduled maintenance at the most cost-effective moment
- Avoid long downtimes and therefore maximize productivity
- Maximize machine uptime and improve operation reliability
Reduce repair costs by repairing equipment before it is compromised
- Get a high ROI in a short period of time
Repair and replacement of equipment are in fact far from the biggest consumption of maintenance time and money. Unscheduled downtime is undoubtedly one of the biggest financial drainers and, by definition, they are unpredictable. Reduce downtime, espacially when unplanned allows companies to save huge amounts of money.
Predictive maintenance tools provide maintenance managers with continuous information about the operating conditions of the machines. They can therefore organize and arrange their maintenance planning in the most cost-effective and convenient way. Through this exchange of information, maintenance technicians can get an overview of their equipment and therefore focus their efforts on areas or machines that require attention and organize maintenance work in the most efficient way.
Machine downtime is necessary from time to time, but planned downtime usually are shorter and less costly since maintenance teams can organize themselves to handle the situation in the most efficient way.
At Insens, we know the importance of predictive maintenance but also the barriers to installing such systems. That’s why we offer a complete solution, from data collection to failure reporting, with our expertise and suggestions on how to address your equipment’s vulnerabilities. All this with a unique ease of installation.
why is our solution a powerful machine monitoring system?
RED is more than just a predictive maintenance system. Most condition monitoring solutions focus only on machine health diagnosis and detecting upcoming failures (e.g. thanks to vibration analysis, lubricant analysis or infrared thermography). Our cutting-edge technology allows us, with the same solution, to provide predictive maintenance diagnostics with high reliability but also to monitor energy consumption, offer optimization suggestions and report the savings made.
We help you eliminate unplanned downtime by predicting machine failure several months in advance.
We can detect sources of energy ineffficiencies and therefore help you minimize those losses to reduce you energy consumption.
We help you through your transition to a more sustainable approach. We can measure and quantify your efforts and therefore help you demonstrate them to your customers.