Machine Failure or Machine Downtime is one of the biggest worries in every manufacturer’s minds. Ranging from trivial to catastrophic, equipment failure can result in repair costs, unplanned downtime, productivity loss, health and safety implications for the workers, and impact production and delivery of services. 

So what exactly is Machine Failure,  what are the common causes for this & how can you ensure that you can stay two steps ahead of this? Understanding this can be your first line of defense against preparing the team of plant engineers against any impending asset failure disasters.

What is Machine Failure?

Machine Failure is an occurrence of a malfunction of any part of an industrial asset due to which it underperforms or stops working partially/completely altogether in the manner it was intended to. 

This means that asset failure does not only mean the sudden machine breakdown to cause unplanned downtime; it can also include intermittent failures that are sporadic or even minor glitches that cause a dip in the performance/output of the equipment. Different aspects of modern AI-driven predictive maintenance programs can help counter these issues, and we will also learn these.

What causes machine failure or downtime?

Improper Operation: This is when failure can be attributed to human errors caused by machine operators. It can be either due to work stress, forgetfulness, distraction, or because the operator might not be adequately trained to handle a particular piece of equipment.

  • The solution: While it is not possible to alleviate this entirely, the traditional approaches of memory-based training (operators expected to remember best practices) have proven to be insufficient. Modern solutions like AI-based decision support systems are offering a new approach. They provide guided workflows to the user in real-time, within the context of the operation. As a result, operators are more consistent and less likely to make mistakes that can lead to undue equipment and safety issues.

Wrong amount of Preventive Maintenance: Preventive maintenance can be both your strength & weakness. Preventive/Planned Maintenance (performed periodically to ensure the machine is in a good condition) when performed infrequently can lead to missing out on early signs of failure, increasing chances of a sudden failure or downtime, reducing asset lifespan, and decreasing efficiency & reliability of the asset, skyrocketing the eventual repair cost.  On the other hand, frequent preventive maintenance results in increased wear & tear on the asset, wasting technician time & efforts, increasing spend nevertheless, since it does not take into consideration the actual operational state of the asset. Further, problems can go unnoticed in between maintenance periods which often forces operators to overdo preventive maintenance increasing the costs and risks of operator mistakes.

  • The solution: Finding the exact amount of preventive maintenance is not easy; so the solution here is opting for predictive maintenance, where the operational state of the machine forms the basis of a maintenance timetable. With the aid of AI, this process can be further calibrated by the system diagnosing the exact cause of failure and its mitigation based on domain knowledge and previous history.

Physical Wear & Tear/Heating up: Every machine has an optimum run life. As the machine gets older, the wear and tear of the industrial machine can cause deterring results. Such wear & tear can result in problems like bearing failure, metal fatigue, corrosion, misalignment & surface degradation.

  • The solution: It is important to track such deterioration. AI-led predictive maintenance can help point to the degradation in machines by continuously tracking machine parameters and notifying plant engineers just in time before they grow to a serious level.

Process inefficiencies: Process inefficiency is defined as taking excessive input (electricity, heat, or other raw materials) to do the same amount of work. Inefficiencies in processes result in excess operating costs and are often an indication of a problem that can increase the chances of accidents/machine failure.

  • The solution: Optimizing equipment performance is a difficult topic as the changes are very small and even accounting for the losses can be tricky. Conventionally, Subject Matter Experts would be called upon by the manufacturers to examine the setup & processes, analyze the operational data and suggest process improvements. But with SMEs retiring and data explosion from the sensors, it is not plausible to stick to that model., A Virtual AI Expert solution like UptimeAI that helps track efficiency with performance curves, and point out small inefficiencies in asset operations can be a lifesaver, especially with the current trend of remote-first operations.

Reliability Culture Failure: A popular terminology in the plant asset management world, reliability culture involves building a synergistic environment between machine operators, plant maintenance teams & operation teams to ensure that temporary patches for repair are done away and insights uncovered are shared & implemented in time to uphold asset reliability—the probability that the asset will be able to work failure-free for a period of time within normal operating conditions.

  • The solution: A simple but effective collaboration tool can save time, digitize knowledge, and encourage cross-pollination of ideas for rigor and best reliability culture.

So what can we conclude?

Machine Failure cannot be 100% controlled. But with a modern and powerful AI-driven solution, it can be predicted, diagnosed, and mitigated in time to make sure that the downtime is minimal & the machine is operating at its best in terms of reliability, availability & performance. AI-driven predictive maintenance can offer a competitive edge to manufacturers via curbing machine failures and taking the operations to the next level by, increasing uptime, efficiency, productivity, and reducing costs.