It is rarely random when an asset fails. Failures are indicators of system weaknesses, wear and tear, or neglected maintenance. Reactive teams differ from high-efficiency operations in their ability to decipher those signals.
The global predictive maintenance market has been experiencing significant growth and is projected to continue expanding in the coming years.
According to a report by IMARC Group, the market size reached USD 12.7 billion in 2024 and is expected to grow to USD 80.6 billion by 2033, exhibiting a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033.
Another analysis by Precedence Research estimates the market size at USD 7.24 billion in 2024, with projections to surpass USD 79.70 billion by 2034, expanding at a CAGR of 27.10% between 2024 and 2034.
Let’s examine how failure occurs, how to interpret it, and how contemporary technologies can transform these setbacks into more intelligent tactics.
To put it simply, a failure occurs when something ceases to function as intended. This could be a motor that vibrates excessively, a cooling system that can’t keep cold, or a machine that won’t start. There are three primary categories of failures:
Inability to Function
Although not as intended, the asset is still functional. Consider a rotating fan, but doesn’t cool.
Hidden Failure
No visible signs until it’s too late. Example: A backup generator that’s dead during an outage.
Potential Failure
Small signs (vibration, heat, noise) that predict a bigger failure. The earlier you catch these, the better.
Every setback creates a trail. Failure codes, which are concise, structured descriptions kept in your CMMS or EAM system, are used to record that trail.
Note : Computerized Maintenance Management System Market to Hit USD 3.72 Billion by 2032, at a CAGR of 10.81%
These codes:
Identify persistent issues (poor calibration, overheating, and user error).
Establish a feedback loop between repair and diagnosis.
Enter data into forecasting tools to prepare for the future.
A good CMMS failure log should look like this:
There are two main ways to handle this data:
Digs into the “why” behind recurring problems
Avoids recurrent breakdowns
Spreadsheets don’t need to be manually sorted when you use tools like Ezist to create automated workflows around these models.
Use these pointers to maintain a strict failure log:
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Results from the Real World: The Significance of Structured Failure Code Strategies for Organizations Report:
If your current system isn’t giving you this kind of insight, it’s time for an upgrade.
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inadequate or postponed upkeep. One major factor is the absence of early detection.
High-volume teams should audit weekly, but ideally once a month.
Indeed. Machine learning is used by predictive maintenance tools to identify problems early.
Make use of sensors and conduct routine checks. Always test backup systems as well.
Of course. Structured failure tracking is crucial in all industries, including manufacturing, healthcare, logistics, and energy.
Take charge of your assets before they fail by using Ezist right now.
