How to Analyze Patterns of Machine Failure to Minimize Unplanned Downtime
Unplanned downtime of essential assets can lead to catastrophic losses for any organization. Unfortunately, while there are recognizable patterns of machine failure, companies rarely use them to adopt a predictive maintenance system.
Instead, most organizations tend to continue their traditions of preventative maintenance. In simpler words, the organization only acts when the machine stops functioning, causing a significant halt in its operations.
While earlier, people had no choice but to engage in preventative maintenance, companies now have access to robust AI-based solutions.
A visual inspection or quality assurance tool can help companies recognize when their assets need maintenance. So, many people often wonder how these AI-driven tools achieve such precision. In most cases, they try to analyze the prescribed patterns of machine failure.
The Six Machine Failure Patterns
The human eye and mind can’t deal with machine-failure patterns, especially considering a vast database of infinite records. Fortunately, with the help of AI-based solutions and advancements in the IoT sector, these patterns are much more visible and preventable.
Here are the most common patterns of equipment failure:
Pattern A: Pattern A of machine failure, also famous as the bathtub curve, signifies a high probability of failures for new machines. It shows a low level of failure in the middle of its life. Finally, there is a sharp increase in failure towards the end of the machine’s lifespan.
Pattern B: In this pattern, a mid-aged machine witnesses a low level of failures, with a sharp increase in machine failures at the end of its lifespan.
Pattern C: This pattern is famous as the “fatigue curve,” which signifies a steady increase in machine failures as time progresses.
Pattern D: Pattern D indicates a low level of failure initially. After this, there is a sharp rise in machine failures followed by a continuous straight line on the graph.
Pattern E: This pattern signifies random machine failures for the entire lifespan of the equipment. There is no significant increase or decrease in failures for the whole duration. Therefore, it’s difficult to predict the pattern of such shortcomings.
Pattern F: Pattern F, or the infant mortality curve, initially signifies a high level of machine failures. When they subside, there is a consistent rate of random failures at specific intervals.
As you can see, these patterns of machine failure are difficult to analyze by an average person. So, relying on predictive maintenance of machines and other equipment would only be possible if a business had computers and AI.
Fortunately, the market has reliable solutions for this grave problem, saving companies from catastrophic drops in performance levels.
The Significance of Machine Failure Patterns
It’s always confusing to rely on predictive maintenance if you don’t have an excellent AI-based, IoT-inspired solution.
But such solutions wouldn’t be of much use if not for these repeating patterns of failure. Of course, the above patterns might need to be clarified. But if combined into a database, a robust AI-based solution can use them to predict when a particular machine might need maintenance.
They also indicate a machine or a unit’s efficiency, capability, or quality. Pattern E, for instance, reflects a poor-quality machine where maintenance is not the solution. In that case, the company should look into other contractors for getting new equipment.
Further, spending a lot on a particular machine won’t make sense even if you rely on predictive maintenance. If it’s causing many problems, the organization may recognize the pattern and find a permanent solution instead.
Machine failures are often unseen by the naked eye. But if you back your system up with an excellent AI-based solution, your quality department assurance won’t make many mistakes.
An excellent tool to carry out the predictive maintenance strategy can do your company a lot of good.