Key takeaways
- Electronic device failures in harsh oil and gas environments are often driven by the combined effects of temperature, humidity, shock, and vibration rather than by any single stressor.
- Directly measuring conditions at multiple locations can reveal localized stresses that conventional system-level monitoring and qualification testing may miss.
- Integrated or embedded sensors using microelectromechanical systems (MEMS) or nanosensors enable multiple location measurements and can give engineers a more detailed picture of the conditions electronics experience in service.
- By combining real operating data with physics-based and data-driven models, engineers can identify emerging failure risks earlier and shift toward a more predictive approach to reliability.
Across the oil and gas industry, electronics are being used in increasingly demanding environments, from downhole tools operating at high temperatures and pressures to subsea systems, offshore facilities, and surface equipment exposed to vibration, moisture, shock, and large temperature swings. In these applications, electronic reliability often directly affects production continuity, equipment performance, and asset availability.
Although the sector has substantially enhanced the reliability of electronics through improvements in materials, design rules, and qualification standards, electronics failures remain a challenge. What’s missing is the ability to accurately capture and model how environmental stressors collectively affect the system under real operating conditions.
Closing that gap means connecting the environment that electronics are designed for with the one they experience in service. By capturing in situ operating conditions and relating them to degradation mechanisms, engineers can build a more accurate picture of how a system's reliability changes over time. That understanding supports better life prediction, earlier identification of emerging risks, and more informed decisions about design, operation, and maintenance.
Electronic failures rarely have a single cause
Electronic devices deployed in today’s oil and gas operations are continuously subjected to environmental stressors that evolve over time. The stressors rarely act in isolation. Instead, they interact, amplify each other, and create complex degradation mechanisms.
Humidity is one of the most critical—and underestimated—drivers of failure. Moisture ingress can trigger a wide range of issues, including corrosion, leakage currents, insulation breakdown, and dendritic growth (i.e., the formation of treelike metallic filaments that can bridge conductors and cause short circuits). More importantly, humidity acts as an accelerator, weakening materials and interfaces and making them more susceptible to other stressors. As the Hallberg–Peck model describes, this effect builds up gradually, reducing a system’s robustness until failure occurs under combined conditions.
Exposure to extreme temperatures creates a similar effect. High temperatures can accelerate chemical reactions and material aging. Temperature changes also create mechanical stress, which becomes more pronounced during thermal cycling—repeated heating and cooling as a system powers up, shuts down, or experiences changing ambient conditions. Because different materials expand and contract by different amounts, repeated cycles can gradually fatigue solder joints, interfaces, and other structures.
Humidity- and temperature-driven degradation is especially problematic in devices exposed to vibration. Continuous movement and mechanical shock expose weak and compromised areas, propagating damage and increasing the likelihood of failure. Such interactions are difficult to predict because vibration is often continuous and multifrequency, whereas shock events are intermittent and random. This unpredictability makes combined environmental and mechanical stressors far more challenging to model than any individual one.
Testing stressors separately doesn’t reflect field conditions
In electronics design, traditional reliability approaches treat environmental factors largely independently. Qualification tests under controlled conditions simulate thermal cycling, humidity exposure, mechanical shock, and vibration. While essential, these tests are inherently limited. They rest on the assumption that engineers can evaluate each stressor in isolation and approximate the stressors’ combined effects using design margins. But, as equipment operators know, this approach doesn't always translate to the field.
Consider, for example, an electronic control board operating in a compact enclosure on an offshore drilling rig. Each time the board heats up during operation, it drives out the moisture it has absorbed; each time it cools, it draws humidity back in, all while machinery around the enclosure produces continuous vibration.
Conventional environmental testing might show that the board can tolerate the specified temperature range and vibration spectrum when each is applied on its own. In service, however, thermal cycling can gradually produce a small crack around a soldered connector joint. Moisture reaches the damaged interface, and vibration continues to propagate the crack until intermittent electrical contact develops.
This type of dynamic environment is difficult to reproduce with conventional test and qualification methods, which poses a challenge for electronics reliability.
Miniaturization makes local measurement practical
Though measuring environmental conditions is essential, it introduces a practical question: how can we capture meaningful data without adding complexity, volume, or new sources of failure to the system itself?
In space-constrained applications, installing additional discrete sensors isn’t always viable because of increases in printed circuit board (PCB) footprint, wiring, packaging requirements, interconnections, and potential failure points. This limitation becomes more significant when the objective is to measure conditions locally, at multiple points within the same system.
For shock and vibration, sensor position can be just as important as overall magnitude. Areas near connectors, mounting points, and large components may experience much greater mechanical loading than other parts of the same board. The board itself also flexes in characteristic patterns at certain frequencies, so vibration can affect one location far more than another.
To a lesser extent, the same applies to humidity and temperature, where local gradients or trapping effects can exist depending on packaging and enclosure design.
Because conditions can vary widely across a single board, sensors need to be both miniaturized and highly integrated. This makes it possible to distribute measurement points throughout an electronic system without significantly altering its architecture. Engineers can then
- place sensors close to critical components and interfaces
- identify localized shock and vibration behavior
- detect temperature or humidity gradients
- compare conditions at different locations within the same assembly.
Rather than relying on a single enclosure-level measurement, embedded sensors can capture local variations that may otherwise go unnoticed. For example, they could reveal that a specific location within the enclosure is simultaneously exposed to a larger temperature swing, elevated humidity, and higher vibration than the assumed system-level conditions. The added spatial resolution gives engineers a much clearer picture of the stresses on individual components and interfaces.
That information can guide changes, such as adding mechanical support, modifying the board layout, or changing qualification tests. During operation, maintenance teams can use the same measurements to identify the units experiencing the most severe conditions and prioritize them for inspection.
Physics-based models and data correlation make measurements predictive
Sensor data becomes most valuable when engineers can translate it into meaningful reliability predictions. Monitoring alone doesn't make a system predictive. Temperature histories, humidity readings, shock events, and vibration spectra describe what the system experienced.
Prediction, however, requires pairing those measurements with knowledge of the design and its failure mechanisms to estimate how reliability will evolve. Depending on the application, that could mean determining
- whether degradation is accelerating
- the probability of failure over a defined period
- the remaining useful life before a component or interface reaches an unacceptable degradation state
- the risk associated with a particular operating or mission profile
- whether a measured condition indicates that inspection, maintenance, derating, or redesign is warranted.
Moving toward predictive reliability calls for a new approach that integrates in situ measurements with physics-based modeling and data correlation. It involves three steps:
- Capturing real operating conditions—Systems must record the environmental history they experience over time to supplement assumptions built into the qualification envelopes.
- Mapping conditions to failure mechanisms—Engineers must link each environmental parameter or combination of parameters to specific degradation processes, including humidity-driven corrosion and leakage, thermomechanical fatigue and cracking, and mechanically driven structural degradation.
- Combining multiphysics effects—Predictive models must account for interactions between environmental stressors, such as hygrothermal effects and vibration-induced crack propagation under thermal stress. These coupled phenomena reflect real field conditions, and representing them accurately is a frontier yet to be fully mastered.
Deployed systems can make reliability models more accurate
Embedded sensors give engineers increasingly granular operating data, which they can use to calibrate simulation models, check assumptions, and identify the combinations of stressors that matter most. The data can also support hybrid approaches that pair physics-based models with data-driven techniques.
Those models must remain computationally manageable, account for differences in materials and system designs, and establish a meaningful link between environmental exposure, physical degradation, and eventual failure.
Over time, reliability modeling can evolve from a one-time exercise during design and qualification to a process that continually improves as field data accumulates. For electronics in harsh oil and gas environments, that shift could give engineers and operators the visibility to better anticipate what comes next.
