The reliability advantage comes from turning data into action
Manufacturers now have access to far more information about the health of their equipment than even a decade ago. Condition monitoring has moved from periodic inspection toward continuous or near-continuous visibility, while increasingly sophisticated analytics can identify changes in machine behavior much earlier. The challenge is no longer simply obtaining data. It is knowing which information matters, understanding what it means and ensuring that the organization can act on it.
Michael DeMaria, Director, Product Management at Fluke Corporation, sees that transition as evidence of a broader change in the role of reliability. Rather than sitting primarily within maintenance, it is becoming connected to production risk, operating cost and decisions about where manufacturers invest their resources.
“Twenty years ago, you were collecting data quarterly, then ten years ago it was monthly, and now we almost have this streaming amount of data,” he says. “Reliability is no longer just the absence of failure. It is about turning a lot of data into insights and metrics. It is about safety, efficiency, continuity, understanding risk and optimizing the asset lifecycle.”
As reliability data moves beyond the people who generate and interpret it, manufacturers also need a way to translate highly technical measurements into information that can support wider operational and investment decisions. “It used to be a tool in the maintenance department, and now it is a tool within the executive discussion,” DeMaria says. “You are taking data from the plant floor up to the executive floor and turning something very technical, such as spectrum and waveform information, into something that has meaning and can ultimately be translated into dollars.”
More data does not guarantee better decisions
The growth in monitoring capability has coincided with a shortage of people needed to interpret what those systems find. Plants are producing more, often with leaner teams, while experienced reliability specialists are not always available in the numbers manufacturers once relied upon. The result is an uncomfortable combination of increasing data volumes and limited capacity to examine them in depth.
One response has been to simplify the presentation of machine health so that teams can see quickly where attention is required. That improves visibility, but it can leave a gap between recognizing that a problem exists and knowing what to do about it. “We constantly see customers with a big panel on the wall showing all the machines as green, yellow or red,” says DeMaria. “We will ask them, ‘You have a machine that is red. How do you turn it back green?’ Sometimes they do not know. They must bring in an expert and capture more data to figure out what is wrong.”
The weakness is not in alerting people to abnormal conditions, but in assuming that an alert is the same as a diagnosis. If the next step is simply to collect more information, the monitoring system has identified a problem without reducing the effort required to resolve it.
AI can help teams process larger volumes of condition data, especially where experienced analysts are scarce, but DeMaria is cautious about treating it as a substitute for expertise. “There is an idea that you can take raw spectrum data, throw it into an AI and ask it to tell you what is going on, and then blindly accept the answer,” he says. “You may be able to operate that way for quite a long time until there is a catastrophic failure that it missed. We see AI as a tool for the analyst, no different from another application that helps guide them effectively and efficiently.”
The consequences become more serious as the importance of the asset increases. A recommendation to shut down a production-critical machine can affect output worth far more than the maintenance intervention itself, so confidence in the diagnosis matters as much as speed. “We have analysts who have been doing vibration analysis for 40 years, and they will still ask another person to look at the data before making a multimillion-dollar judgement call,” DeMaria says. “There is tremendous pressure when downtime is measured in minutes or hours and you are making a recommendation about whether that machine needs to come down.”
Build capability before trying to monitor everything
The importance of critical equipment can tempt manufacturers to begin a new reliability program with the machine that presents the greatest production risk. DeMaria argues that this is not always the best place to start because the most critical asset may also be the most difficult to diagnose. “If you are brand new, do not necessarily start with the most complicated machine,” he says. “Start with something where you can understand what vibration is supposed to look like, build your knowledge and then expand.”
That puts greater emphasis on defining what the program is expected to achieve before deciding how much technology to deploy. Rather than starting with the number of sensors or assets that can be connected, manufacturers need to establish what they want to improve and what evidence will show that the program is working. “We keep bringing people back to the question: what do you ultimately need to get out of this program?” DeMaria says. “What do you need to report on to demonstrate that the program is successful? Once they start looking at it that way, they begin to understand the strategic reason for condition monitoring and that changes the scope.”
The same discipline matters when programs scale. Monitoring hundreds or thousands of assets may be technically straightforward, but every meaningful alert potentially creates work for somebody. Deploying faster than the organization can analyze findings, prioritize interventions and complete repairs simply moves the bottleneck elsewhere.
Enterprise scale introduces another challenge because plants often develop different reliability practices over time. Individual facilities may use different tools, terminology and thresholds, leaving senior management without a consistent way to compare results or understand where investment is having an effect. Standardization becomes especially important when reliability programs span multiple plants, because inconsistent measures make it difficult to compare performance or understand where maintenance investment is delivering value.
“One plant manager told us that he did not believe in condition monitoring because he did not think they were getting enough meaningful information,” DeMaria says. “But when you looked at that plant’s spending on replacements and repairs compared with the other plants, it became incredibly clear. You could only see that once the organization had standardized the platform and the metrics.”
Diagnosis only matters if it changes the work
The value of condition monitoring is ultimately determined by the quality of the response it produces. Finding a developing fault earlier creates little advantage if the information remains within an analytical system or does not reach the people responsible for planning and executing maintenance.
That connection becomes more important as monitoring frequency rises. Under periodic inspection, a diagnosis might remain broadly unchanged until the next data collection round. With daily or continuous monitoring, an asset can deteriorate while maintenance work is already being planned.
“If I am analyzing a machine, I want visibility of whether this problem is already known and whether action is already being taken,” DeMaria says. “Then, when they repair the machine, I want to know what they found. Was my diagnosis correct? That helps train the system and it helps train me.”
Earlier identification also creates practical options that disappear as failure approaches. Production can be rescheduled, parts can be ordered and maintenance can be coordinated around operating requirements rather than forced into an emergency response. DeMaria says some customers will not close a work order until new condition data confirms that the repaired machine has returned to an acceptable state.
That closes the loop between detection, diagnosis, intervention and verification. It also explains why reliability can create value beyond avoiding breakdowns. The advantage comes from giving operations enough warning and confidence to manage equipment risk with greater control.
“I believe it is more about the ones that can turn it into action,” DeMaria concludes. “You do not necessarily have to have the most advanced set of data. If you have the most advanced data and you do not know what to do with it, it is not doing you any good.”
As manufacturers connect more equipment and generate more condition data, that distinction will become increasingly important. Reliability advantage will not come from the volume of information a plant can collect, but from its ability to convert the right information into decisions early enough to change the operational outcome.
