Prior to the Second World War, rugged, slow machinery with basic control was the norm, because back then, production demand was meagre, and maintenance downtime wasn’t an issue for most manufacturers.
Post-war, production demand increased, leading to over-utilization of the available machines. This increased usage resulted in more wear and tear, thereby increasing the cost to fix machinery, and creating demand for better maintenance practices. The rebuilding of industries opened the doors to a new chapter in the history of equipment maintenance and led to the development of preventive maintenance.
By the 1980s, the complexity of industrial systems increased, and there was a demand for greater reliability. As people became aware of process failures, risk assessment and improved management techniques see the light of the day. Maintenance of quality standards, expert systems, condition monitoring, and end-to-end predictive maintenance also emerged in the industrial scene.
Issues With Traditional Maintenance Systems
While industries realize the importance of maintenance, the fact is that most traditional maintenance techniques lack structure. Across sectors, most maintenance activities involve a list of tasks that need to be checked. The ground staff do not have detailed knowledge of the acceptable limits and end up adopting a ‘tick-and-flick’ maintenance routine.
This lack of structure also causes the machines to be viewed as standalone objects. As such, the person who is in charge of equipment investigates its running conditions. While every piece of equipment is tracked, no one looks at the bigger picture to evaluate the coordination of the different equipment involved in the industry.
Industries also report instances where operations are reluctant to take a piece of equipment out of service for maintenance. Without proper maintenance, chances of complete equipment failure increase. This causes a higher downtime and a disruption in the service line.
Debunking the Myths
The shortcomings in conventional maintenance methods have paved the way for predictive maintenance. Sadly, several myths have emerged surrounding these. In this article, we will debunk the top ten unwarranted myths around this style of industrial equipment maintenance.
1. Predictive Maintenance Is Expensive
Traditionally, equipment maintenance was an expensive affair and needed an ongoing cost for individual analyses. Depending on the industry, extra staffing may also be necessary. A detailed analysis of the different maintenance styles will reveal that the long-term cost associated with predictive maintenance is significantly lower.
2. It Is Difficult to Implement
Recent advancements in artificial intelligence have made the industrial implementation of predictive maintenance a simple task. Several smartphone-enabled devices facilitate continuous monitoring of industrial systems. The intuitive interface and simple handling ensure that your ground-level team can easily collect the vibration data.
3. You Need to Be a Data Management Expert
Unlike the traditional systems, modern predictive maintenance units do not come with user-dependent tablets. Machine learning has removed expert-reliant raw data and enabled automated diagnostics systems. Thus, you do not need to be an expert to make sense of the predictive maintenance data. The system will provide you timely actionable insight about your equipment, and you can act on it to reduce the downtime.
4. Predictive Maintenance Is Applicable Only for Expensive Equipment
These days cost-effective PdM systems are available. Understand that irrespective of the cost of your equipment, every item serves a function. Considering that all industrial equipment is likely to face technical issues at some point in its life cycle, it is wise to take proactive maintenance measures. While traditional maintenance techniques seem viable for the cheaper equipment, these accumulate to a higher maintenance cost over time.
5. Predictive Maintenance Is Not Necessary for Equipment Under Warranty
When you take good care of your equipment from the first day, they are maintained at peak efficiency. This improves their longevity, and the machine can work for decades. Moreover, the predictive analysis gives timely insights into your machine’s health and enables a reduced energy consumption.
6. Preventive Maintenance Is Easier
In preventive maintenance, you need to take urgent calls and fix issues in the machines once they affect the performance. The emergency scheduling and unprecedented affect the operations. Opting for predictive analysis allows you to leverage asset performance management techniques and schedule maintenance of systems in advance.
7. The Technology Is Not Reliable
The last decade has seen tremendous progress in technology, and electronic sensors are capable of just about anything. These are more sensitive than the human body and can discover problems in your machinery well in advance. HAC based on the Internet of Things is another highly reliable advancement that can help you reduce unplanned equipment downtime.
8. Maintenance Is An Annual Affair
Conducting a benchmark test every year and sporadic data collection will not suffice. If you are keen to make the most of predictive maintenance, you must recognize that this is a regular activity. Regular analysis of the health of your equipment will keep you better informed to make strategic decisions.
9. Collecting and Filing Data Is Predictive Maintenance
Predictive maintenance is more than taking reading off your equipment. To reap the benefits of predictive maintenance, you must use an online, dashboard-style analysis system. That way, all the data will be easily readable, and you can make informed decisions.
10. Predictive Maintenance Is Not Feasible for Systems With Built-In Redundancy
With predictive maintenance, you can expect a 30% reduction in maintenance costs. As a result, you save at least $1 per square foot of commercial facility. Intuitive handheld devices and continuous monitoring systems improve the efficiency of the system while saving on the cost. Thus, irrespective of whether your system has built-in redundancy, predictive analysis is a viable solution.
Thus, you see that with the industrial Internet of Things (IIoT), a business can optimize all aspects of the manufacturing order. Leveraging industry 4.0 technology lets you access real-time data and provides insights to streamline business efficiency.
As you look to leverage such advancements to improve the profitability of your business, end-to-end predictive maintenance will put you on the path to business growth.
Find out how Eugenie.ai taps into the magic of AI to eliminate downtime and other maintenance issues, thereby boosting your operations.
If this inspires you to join us in the challenge of making predictive maintenance mainstream, check out our opportunities, and connect with us.