Artificial intelligence (AI) is changing how companies think about reliability. Nearly every wireless vibration system promises “AI-powered insights” leading to “smart maintenance decisions.” But in practice, AI is only a tool. The results of AI are only as good as the data it receives. If a system doesn’t capture the right signals, no amount of analytics can fill the gap. As a result, the maintenance team may end up reacting to failures instead of predicting them.
The real differentiator in predictive maintenance (PdM) isn’t AI, but rather the intelligent combination of quality data and expert domain knowledge—coupled with AI.
When “Smart” Sensors Are Blind
Every system claims to have a smart sensor, and every sensor captures useful information. Even the most basic system will tell you if the shaft is unbalanced, the coupling is misaligned or the structure is loose. These are important faults to know about—but they’re also the easiest ones to detect.
This fact makes it easy to run a successful—although inconclusive—pilot. During an initial trial, any system will identify plenty of obvious issues across the plant. It feels like proof of success, but the real test comes later. Many sensors are blind to the signals generated when their critical assets have a developing bearing or gear defect. However, these are precisely the failure modes that will most likely shut down production—the “process killers”.
Hidden Signals from the Process Killer
All machines generate vibration signals across a broad range, from low frequency to very high frequency. The question is: which frequencies matter most?
Lower frequency vibration correlates to movement of the machine—the rotation of the shaft and the alignment of its components. In contrast, higher frequency vibration is caused by impacting—for instance, the sharp, short bursts that are generated when a rolling element passes over an outer race spall.
To detect those impacts, you need to measure the vibration in a high-frequency range and with a correspondingly high sampling rate. The higher the sampling rate, the higher the data quality. With standard vibration sampling, however, these sharp impacts actually register as smooth movement—even though serious mechanical damage is progressing toward impending failure.
Many wireless sensors, therefore, aren’t even capable of capturing the full vibration picture. Using a lower frequency range (and sampling rate) can render the system blind to the high-frequency signals from a damaged bearing and leave plant operation exposed to an unscheduled outage. It should be noted that the vast majority of available systems use third-party signal processing that further limits their ability to detect these signals. The result is an incomplete view of machine health and a greatly reduced ability to detect bearing and gear defects, as well as other process killers.
Moving Up the Failure Curve
Reliability engineers often refer to the P-F curve, the elapsed time between the onset of a failure mode (e.g., P = Potential Failure) and the resulting production outage (F = Functional Failure). The goal is to detect problems as early in the curve as possible. This provides additional time to plan and avoid unscheduled outages.
Systems that can’t detect high-frequency impacting may miss these defects entirely. Or, if they do detect them, it will be much later in the failure curve, so there’s little or no time to schedule maintenance before the asset fails.
Processing the raw vibration signal with high-frequency sampling changes everything. The resulting information not only provides the nature of the problem, but also the defect severity, so that you know how far it has progressed. That’s the difference between reacting to failures and truly predicting them.
Functionality and Price Don’t Always Match
With a wireless vibration device, it’s tempting to assume that expensive means better. However, the effectiveness of a wireless vibration sensor has little to do with its price. What determines its success is intelligent design—how the sensor captures, processes and transmits data.
A well-designed system can deliver high-quality data without inflating the price. A poorly designed system may be expensive and still be blind to these critical defects. The distinction comes down to engineering choices:
- Can it sample fast enough to capture high-frequency impacts?
- Does it accurately measure vibration amplitude?
- Is the raw analog vibration signal accessible to the AI algorithm?
Without these fundamentals, even the best AI model is working with partial information. The output might look sophisticated, but the diagnosis is built on incomplete data.
Beyond AI: Data Quality, Domain Knowledge and Diagnostics
The performance of a PdM program isn’t determined by the AI algorithm alone. It results from a collaboration between quality data, extensive domain knowledge and good diagnostics. Since vibration signals are driven by physics, the biggest benefit of AI is expanding coverage. It does not fundamentally alter the process of vibration analysis. The concept of “machine learning” is almost misapplied here, since the diagnostic principles underpinning vibration analysis have been well understood for decades. In fact, if a given system actually claims to be “learning” as it operates, it actually means that it was not founded on well-established failure mode analysis to begin with.
Therefore, the key differentiator between the output of reliability systems is whether they were designed by experts who understand machines and machinery analysis. In a well-conceived system, it will collect high-quality data at a high sampling rate (typically above 100 kHz) to provide the earliest and most accurate signs of mechanical stress — and, importantly, the severity of the defect. Domain expertise ensures that these signals are interpreted correctly. This key feature shifts maintenance from hindsight to foresight.
While AI remains a powerful tool, it cannot substitute for high-quality data and extensive domain knowledge. The ability to extract key information from the raw vibration signal is what makes prediction possible.
As a final note, PdM of any kind is only valuable if you have a process in place to act on the information gleaned from the vibration signal. This is a key issue that needs to be answered before implementing any system. When evaluating a system, these are good questions to ask:
- Is there an option for advanced troubleshooting?
- Can I have a skilled analyst come on-site to validate an AI diagnosis?
- What other predictive technologies can be combined with vibration to confirm the need to shut down for maintenance?
Click here to learn how you can translate these principles into practice and take your PdM program to the next level.
