Studies show that between 30-40% of industrial sensor and measurement data requires significant cleaning or validation before it can be used reliably. And research from Gartner estimates that poor data quality costs organizations an average of $12.9 million annually, with industrial operations particularly affected due to their dependence on real-time sensor measurements.
When industrial sensors gradually degrade, AI models drift from reality, analytics become unreliable, and cloud platforms process corrupted information – all without triggering traditional alarms.
Understanding Sensor Failure in Industrial Plants
Industrial sensor failure rarely happens dramatically. Instead, sensors degrade gradually through several common failure modes:
- Sensor drift: Sensors gradually move out of calibration, producing readings that shift by small percentages over weeks or months.
- Signal noise and interference:Electrical interference, sensor degradation or fouling increases signal noise, making measurements less reliable without completely failing.
- Intermittent failures: Sensors work normally most of the time but periodically produce incorrect readings or freeze at constant values.
- Communication failures: Data transmission issues cause missing or delayed sensor readings without obvious hardware problems.
Traditional methods rely on threshold-based alarms that only catch complete failures. Detecting these gradual patterns before they impact operations is exactly what AI and analytics programs need to scale successfully.
Figure 1: Aperio uses unsupervised machine learning to detect and isolate a wide range of sensor failures.
Why Data Quality Determines Whether Digital Initiatives Scale
For IT and Data Teams: When industrial sensors gradually degrade, AI models trained on corrupted data produce unreliable outputs. Undetected sensor failures mean AI systems learn from faulty measurements rather than real process conditions. By the time teams realize their models have drifted, significant investment is lost. More critically, the organization loses confidence in AI-driven decisions, creating hesitation or even resistance to expanding these digital initiatives.
For Operations Teams: When sensor data gradually degrades, operational decisions based on analytics become unreliable. Teams lose confidence and revert to manual verification, undermining digitalization efforts.
Why Traditional Sensor Failure Detection Methods Don’t Work Anymore
Traditional sensor failure detection methods rely on threshold-based alarms that only trigger when sensors exceed pre-defined limits – detecting complete or dramatic sensor failures but missing gradual degradation that spans months. In the past this was acceptable – the most critical sensors were checked and recalibrated at set intervals, while the rest may have been left unchecked unless something significant was detected. However two new conditions that make this approach not feasible anymore:
- The increasing amount of data points:
Modern industrial facilities now monitor tens of thousands or even millions of sensor data points – far beyond what was typical a decade ago. Digital transformation initiatives, IoT deployments, and advanced process control have dramatically expanded the number of sensors requiring monitoring. Manually tracking this volume of data is impossible, yet each additional sensor represents another potential failure point. - The AI and digitization era:
AI and machine learning models require high-quality training data at scale. Research from IDC shows that 60% of AI and analytics projects fail to move beyond pilot stage, with data quality among the primary obstacles. Unlike traditional process control that could tolerate some gradual sensor failures, AI models learn from every data point—meaning gradual sensor degradation directly corrupts model training. When a model trained on data from 50 carefully monitored sensors is scaled to 5,000 sensors across multiple sites, even a small percentage of degraded sensors can render the entire system unreliable.
Wide-scale manual calibration is too resource-intensive: industrial facilities typically calibrate only 15-20% of sensors on regular schedules, leaving the majority of sensor failures undetected until operational problems surface. These conditions require a much more precise and automated approach to detect sensor failures at scale.
This creates a data trust gap: The period between when sensors begin failing gradually and when anyone detects the sensor failure becomes a scaling blocker – organizations hesitate to expand successful pilots because they can’t verify data reliability across their entire sensor infrastructure.
The New Approach: Automated, Scalable Sensor Failure Detection
APERIO DataWise that functions as a data assurance layer between industrial sensors and your analytics, AI, and cloud platforms.
The platform monitors thousands or millions of industrial sensors simultaneously, identifying a wide range degradation patterns with pinpoint accuracy. It can also predict failure severity and prioritize issues based on their impact, enabling teams to shift from reactive firefighting to proactive maintenance—addressing critical sensor failures before they corrupt AI models, compliance reporting, or process control.
This automated, scalable approach provides visibility into data trustworthiness – from individual sensors to entire production lines.
Want to learn more? Book a demo with APERIO.