Accurate and timely detection of a mechanism degradation when it’s just beginning is the holy grail of effective maintenance. It’s the ultimate way to fully address the inevitable industrial degradation, with minimum downtime and minimum unnecessary capital expenditure.
Put that in contrast to preventive maintenance, which often has you replacing expensive parts and machines that can serve you well for many more years to come, just because someone scheduled a replacement.
But predictive maintenance isn’t perfect either, especially when it’s operated by humans.
The Problems with Human-Operated Predictive Maintenance Approaches
Predictive maintenance approaches are capable of detecting assets’ degradation, but most solutions available today are manually operated, which means they’re inevitably limited.
Human-Operated predictive maintenance…
1. Is only performed periodically, in predefined intervals, which means they miss out on changes that happen in between and therefore miss out on opportunities to save machinery functionality.
2. Requires dedicated testing and equipment, which adds significant cost and time waste to your operations.
3. Requires well-trained human personnel to perform it. Despite the great time and money invested, degrees of success vary.
4. Ignores most of the data your machines collect. Given the sheer amount of big data collected from even a single machine, and the very minute data fluctuations (that form a pattern that is indicative of an upcoming failure), the task is usually above and beyond what any human can perform.
What True Predictive Maintenance Requires
In other words, if you’re doing manual predictive maintenance, you’re not really predicting holistically. You’re leaving too much data behind, which is why you’re leaving tens of millions of dollars on the table each year due to machine downtime. You also risk predicting things wrong, and worsening the situation instead of improving it.
To change that, you need to give your operations what they need for a true predictive maintenance:
1. Continuous 24/7 monitoring
2. Analysis of very large amounts of data in real time, and most of all…
3. Identifying complex failing patterns, in even minuscule fluctuations, without the need of any human eye or brain in the process.
An Invitation to Listen to Your Machines (and Get a Free Consultation)
We founded Presenso because we were tired of seeing so many industrial companies make so much effort, only to leave monumental amounts of money on the table every single year. Our hard work created the first system in the world that provides modern industrial firms with the full promise of true predictive maintenance.
Operating on the cloud, our system records, in real time, the continuous big data flow generated by each machine’s broad array of sensors and operational log files.
These data are then run through a series of unique machine and deep learning algorithms, designed to predict and accurately alert you about future failures. That means you can plan in advance and perform condition-based maintenance tasks only when they’re required, rather than passively react to failures after they’ve already occurred, or change machine parts that don’t need changing.
And there’s more.
We’re currently working on the breakthrough Machine Crowd Sourcing – the first-ever solution-recommendation layer that’s based on your operations’ failure signatures, and on which solutions worked for your machines in the past.
The more the system learns your operations, the more intelligent it becomes. That means your operations will constantly reduce machine downtime, shorten maintenance work cycle time, and collect best practices, to save you even more significant sums of money down the line.
And all this in one solution that can start working within 2 weeks of deployment. No human in the loop, no expert knowledge needed, no time taken away from your current operations.
Want to see how true predictive maintenance can help you increase machine up time?
Contact us now at email@example.com for a free consultation, and we’ll help you save millions of dollars a year.