Overview
Sensor selection in engineering projects involving sensing is frequently viewed as a logical and impartial procedure. When everything "matches," the decision is deemed safe by engineers after comparing datasheets and aligning accuracy, range, interfaces, and electrical parameters.
However, in reality, a lot of sensing projects succeed in the design phase. They don't work after deployment.
It is possible that the sensors are still active. The system might still be operational. However, data becomes unstable, maintenance effort increases, and performance gradually drifts away from what was expected. Poor hardware quality is rarely the root cause of this problem. More frequently, it is a flawed assumption:
Meeting specifications does not imply being prepared for real-world situations.
This article investigates why sensing projects often appear to be correct during reviews but fail in actual deployments—as well as how engineering teams can transition from specification-driven selection to reality-oriented design.
Why do projects appear fine during the review phase?
Sensing projects exist in an idealized world during review sessions. Test conditions are predetermined, variables are restricted, and system boundaries are clearly defined. Sensors behave predictably in these conditions, and performance appears stable.
Specifications are not inherently incorrect; however, they are almost always derived from controlled test environments. Power is assumed to be stable, electromagnetic noise is low, mounting is standard, and temperature and humidity remain relatively constant. In practice, these assumptions rarely hold true for long.
This leads to a false sense of confidence.
If the sensor performs well in the lab, it should perform similarly in the field.
Another common problem is an excessive emphasis on a small number of headline metrics. Accuracy, resolution, and detection range frequently dominate selection decisions, while long-term stability, interference tolerance, and environmental robustness are given less consideration. These trade-offs are easily overlooked during reviews but become critical after deployment.
Experience bias is another factor to consider. A sensor that performed well in a previous project is frequently reused without first determining whether the new environment, installation conditions, or operating patterns are truly comparable. Small contextual differences are often overlooked, despite their ability to have a significant impact on sensing performance.
Structural constraints of specification-driven selection
Specifications describe capabilities rather than survivability.
Sensors in real-world systems are subjected to combined stresses over time rather than single extreme conditions. Temperature cycles, vibration, electromagnetic noise, dust, moisture, aging, and installation variation all interact over time. These factors, taken individually, may appear manageable. They frequently exacerbate instability when combined.
The majority of datasheets do not describe how performance degrades when these effects are combined. They set boundaries, not behaviors.
Another issue is that many commonly used parameters are not defined in a consistent fashion across suppliers. Two sensors may appear to be equivalent on paper while being validated using different assumptions or test methods. These distinctions are rarely apparent during selection, but they become clear once systems are deployed.
As a result, failures frequently do not manifest as obvious breakdowns. Instead, they manifest as untrustworthy data, requiring more maintenance effort and making systems difficult to trust or debug.
Typical Failure Patterns After Deployment
Sensing failures following deployment are typically gradual rather than sudden.
Changes in the environment are among the most common triggers. Temperature variation, humidity, and electromagnetic conditions can all gradually shift measurements away from their original calibration. Because the system continues to function, this drift may go unnoticed while still influencing downstream decisions.
Installation variability is yet another unavoidable factor. Real-world installation seldom matches design drawings exactly. Small variations in angle, mechanical coupling, or proximity to interference sources can all have a significant impact on performance, even when the sensor is functioning properly.
Long-term stability is an extra challenge. System behavior changes over time due to component aging, material fatigue, contamination, and power degradation. These effects are seldom captured in short-term specification tests but frequently dominate field performance.
What Engineering Teams Need to Validate Beyond Specifications
Reducing deployment risk does not necessitate additional parameters, but rather different validation priorities.
Instead of focusing solely on whether a sensor can achieve a target value, teams should consider whether it can perform consistently under real-world operating conditions. This includes validating performance under various environmental conditions, system integration, and extended operation.
In many applications, balancing accuracy with stability, robustness, and power behavior produces better results than pursuing theoretical peak performance. A sensor that is slightly less precise but consistently reliable frequently provides more value than one that is perfect on paper but fragile in practice.
Furthermore, sensors should not be evaluated in isolation. Power supply behavior, communication interfaces, mechanical structure, and software processing are all factors that affect real-world performance. This system-level interaction must be taken into account during selection and validation.
From "Meeting Specifications" to "Surviving Reality"
Mature sensing projects use a different approach to selection.
They don't just ask the question.
Does this sensor meet the specifications?
Instead, they ask
Will this system continue to produce reliable data in the real world over time?
This shift necessitates accepting uncertainty, validating assumptions with representative pilots, and prioritizing system robustness over isolated performance metrics. Specifications are still important, but they are treated as a starting point rather than a guarantee.
Summarization
Underestimating the complexity of the real world is often the cause of sensing project failures rather than technical ineptitude.
Risk is subtly incorporated into the system when specification compliance is viewed as a success. Sensing systems can only provide long-term value by switching from parameter alignment to reality-oriented validation.
In the real world, a system's ability to function consistently in the face of flawed and unpredictable circumstances determines its success rather than datasheet checkmarks.
That's the real difference between reality and specification.