Relying on time-based maintenance may be doing more harm than good
Asset maintenance has long been governed by the calendar with many organizations adhering to rigid schedules and performing repairs and replacements at fixed intervals. This strategy, known as time-based maintenance, is designed to prevent unplanned failures and sustain an asset's useful life.
However, while proactive, time-based maintenance can be inefficient. It also ignores the reality that 90% of machine failures are random and not time-based.
Understanding the limitations of time-based maintenance
Time-based maintenance is best understood through the analogy of changing the oil in a car. Most manufacturers or mechanics will recommend that a car’s oil is changed at a set frequency based on miles or time—regardless of how the car has been driven or performed. This is done not because the oil is known to have degraded, but because it’s believed that this will help prevent failure.
In an industrial setting, this approach leads to a maintenance program based on averages and assumptions rather than actual asset condition. These averages are often derived from Original Equipment Manufacturer (OEM) simulations or studies and do not account for the specific operating context of your machinery.
5 risks of time-based maintenance
Adhering strictly to a schedule may seem like a low-risk strategy, but it can lead to excessive costs and risk of failure. Some of the common challenges related to time-based maintenance include the following:
1. Capital inefficiency and over-maintenance
Time-based maintenance can lead to a waste of time and resources spent on healthy assets. For example, when a component is replaced simply because a schedule dictates it, the organization loses the remaining useful life of that part. This over-maintenance wastes time, spare parts, and budget unnecessarily.
2. Risk of random failure
Time-based maintenance does not account for random failures or rapid degradation caused by process changes. If a machine develops a fault shortly after a scheduled inspection, it may run to failure before the next interval. This leads to unplanned downtime—the very outcome the schedule was designed to prevent. Without real-time visibility into asset condition, operators remain unaware of degradation or potential failures between calendar checks.
3. Hidden maintenance costs
Direct maintenance costs are predictable and visible. However, there are many hidden costs that are incurred when failures occur in between planned maintenance intervals. These hidden costs, including lost production and impacts of environmental and safety risks, can be up to five times higher than direct maintenance costs.
4. Increased safety exposure
Manual maintenance or data collection can introduce a degree of risk, particularly in automated factories or confined, hard-to-reach locations. In addition, intrusive inspections of healthy machinery can introduce human error during reassembly, potentially causing a failure in a previously healthy asset.
5. Inefficient allocation of skilled labor
In a resource-constrained environment, assigning skilled technicians to perform routine inspections on healthy equipment is an inefficient use of human capital. Time-based maintenance consumes valuable hours that could be reallocated to initiatives that enhance performance and production.
Shifting from time-based to data-driven reliability
Advancements in condition monitoring and prescriptive analytics enable a highly effective alternative to time-based maintenance. With condition monitoring, organizations can move beyond the "oil change" mentality by using sensors to measure vibration, temperature, and speed in real-time, ensuring maintenance is only performed when necessary. Prescriptive analytics take this approach a step further, combining AI with physics-based models to diagnose root causes and recommend specific fixes.
Our customers are already realizing these benefits. Consider the case study of a major oil refinery and petrochemical producer. The company previously relied on different maintenance strategies, like route-based, preventative, and run-to-failure, and used portable data collectors and manual analysis tools. This led to inconsistent machine data, delayed repairs, and occasionally unexpected or extended outages. The team knew they needed to change their maintenance approach and that access to real-timemachine data could save them considerable costs and inefficiencies.
To solve this challenge, the refinery piloted Machine Health powered by Augury which provided real-time insights into asset health. This included prescriptive alerts that identified issues and recommended courses of action to help the refinery plan and execute repairs.
The impact was immediate: the refinery achieved nearly a 4x ROI within six months just in repair savings. Over two years, they also successfully reduced maintenance costs by 72% and avoided over 1,800 hours of machine downtime. With access to steady and reliable data, they are also no longer firefighting or acting on outdated information. Now they prioritize maintenance tasks and work as a connected team.
Cordant™ Machine Health powered by Augury facilitates this advanced level of asset management by pairing robust wireless sensors with purpose-built AI trained on over 3 million hours of monitoring data across more than 120,000 machines. Because the AI utilizes a massive library of component-specific failure modes, it can tell maintenance teams exactly what is wrong and how to fix it. This restores trust in system alerts and eliminates the hazardous, manual data collection of the past.
Ultimately, the shift to prescriptive analytics with Cordant™ Machine Health renders the rigid maintenance calendar obsolete. By replacing arbitrary maintenance intervals with actionable, data-driven insights, organizations can eliminate expensive habits like over-maintaining healthy machines and reduce risk and cost.
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