The Shifting Sands of Maintenance Surprises
Key takeaways:
- Condition monitoring offers a path to condition-based maintenance but its setup time and effort are prohibitive.
- Lack of record keeping, subject matter expertise, and historical context to accumulated data are considered barriers to bringing about data-driven automation.
- The need of the hour is smart condition monitoring - automatically programmed, unattended, plant-scale anomaly detection is the key.
The main challenge facing manufacturing today is talent shortage. And yet the demands on manufacturing to improve productivity and quality remain quite consistent. The bulk of the productivity challenge is caused by new abnormal conditions arising somewhere in plants at any given time. How are we to cope with so many surprises in operations and maintenance practices? Can we even do that when we haven’t been able to prepare for AI to the level that experts claim to be necessary?
Condition monitoring is the path to go from scheduled maintenance to condition-based maintenance. However, classic condition monitoring methods are not working. Such methods need specialized training, tools, and, most importantly, a lot of time that the industry simply does not have. Even if some of these resources are mobilized, the most a model-based approach can do is track known failure modes that have happened in the past.
The challenge
To understand how time and talent can be potential bottlenecks to productivity gains, let’s take a typical integrated steel mill as an example – it can generate upwards of 20,000 signals from multiple sources. By spending a lot of man hours of an already stretched workforce you might be able to monitor a few hundred of these signals for certain behaviors of interest with custom models. This means 98% or more signals go unmonitored. Just imagine the number of missed opportunities for getting early warnings about excursions and other faults.
Your original goal was to monitor every single signal, for every possible failure, at all times! But for the kind of scale we just described, it’s very difficult to anticipate every signal variance that might indicate a problem. A more intelligent approach is needed and that is what leads us to smart condition monitoring.
The reality
Manufacturers are convinced that smart condition monitoring is real and it can improve their operations. However, in the course of helping manufacturing organizations apply smart condition monitoring solutions, we have seen several of them facing the above challenge. With our new, patent-pending self-supervised anomaly detection approach, you do not need to spend time defining which signals are the right ones to monitor, which periods of time correspond to normal behavior, or which groups of signals are primary contributors to a particular failure. The system learns all of that by itself thereby reducing upfront effort, time spent in training on specialized tools, and the need for subject matter expertise at every stage. We only need minutes to days of data instead of years of accumulated historical data.
Falkonry’s analytics approach also overcomes the challenge of spurious benign conditions and new operating modes as well as a complete overhaul by automatically incorporating new behavior into the baseline including any ignored anomalies. This method removes significant effort from ongoing maintenance of a smart condition monitoring solution.
What’s in it for you?
You don’t have to cough up prior occurrences of problems, nor do you have to perform labeling of the data. There’s no need to prep the data or document all the failure modes in advance. Plus, you get value out of the 99% of your automation data which you have not been able to exploit so far.
If that sounds like something you’d want to provide your plant then, we should talk.
