Five common mistakes in industrial temperature monitoring

In industrial production, effective temperature and humidity monitoring is more than just installing sensors.

In industrial production, temperature and humidity play a bigger role than many realise. They influence how materials behave, how stable processes run, and whether products meet the required quality standards.

Even small changes in ambient conditions can have serious consequences. Materials might react differently, production steps can get disrupted, and the final product may no longer meet specifications.

Yet mistakes in environmental monitoring happen more often than expected. The following five examples show the most common pitfalls when monitoring temperatures in industrial environments — and how to avoid them with the right approach.

Mistake 1: Skipping regular calibration

Temperature sensors, though highly reliable in design, are subject to drift. Drift can be gradual or an abrupt shift in the measured value, and can arise from several mechanisms: mechanical strain such as vibration or constriction of sensing wires; thermal cycling that induces repeated expansion and contraction of the platinum element; contamination of the sensor surface with foreign atoms; or moisture ingress that alters insulation resistance. Depending on the cause drift can be temporary, occurring only during the process, or permanent, irreversibly changing the calibration baseline and detectable only through recalibration.¹

Longitudinal studies confirm that drift is not merely a theoretical risk but an observed reality. Investigations of industrial platinum resistance thermometers have shown that fewer than 15% maintained calibration stability within ±0.005°C after thermal treatment and handling, while the majority exhibited measurable shifts, often linked to strain or humidity exposure.² Such findings demonstrate why drift is considered an unavoidable characteristic of temperature sensors, regardless of manufacturing quality.

Given this inherent instability, calibration intervals play a decisive role in maintaining measurement reliability. While some operators extend intervals to reduce cost or downtime, evidence suggests that recalibration should be performed every 12–24 months, depending on application and environmental stress factors.³ Intervals longer than two years are associated with a markedly higher risk of undetected drift beyond acceptable tolerances. In highly sensitive production environments, where even deviations of a few hundredths of a degree can affect process stability, annual or shorter intervals are recommended.

Operational concerns about downtime during calibration are valid but solvable. Modern hot-swap capable sensors allow replacement of probe modules without interrupting ongoing measurements, thereby enabling regular calibration without data gaps or production stoppages. By combining systematic calibration schedules with drift-mitigating hardware strategies, plants can ensure that temperature monitoring remains a reliable foundation for quality assurance and regulatory compliance.

Mistake 2: Incorrect installation

Even the most advanced monitoring system cannot deliver accurate results if it is installed in the wrong place. A common mistake is mounting a sensor in an area that does not represent the actual conditions in the monitored space, resulting in poor measurement accuracy and even distorted datasets.

For example, placing it directly under an air conditioning outlet or too close to a heating source will produce readings that are not representative of the room as a whole. In storage areas, sensors are sometimes fixed too high or too low, causing them to miss the actual temperature range relevant to the products. The result: deviations go unnoticed until they cause quality issues.

Another mistake is sensor placement without account to large machines, metal structures and dense shelving. Such obstacles can create signal shadows that interfere with data transmission, which leads to incomplete data or delayed transmissions.

The third mistake is often the reason for the previously mentioned ones. Temperature monitoring is often not considered early enough in the planning of a facility or production line. Without integrating it into the infrastructure design, sensors may end up in suboptimal locations, or cabling routes may be impractical.

To avoid sensor placement issues, it is important to carry out detailed temperature mapping before installation. This process identifies warm and cold spots, ensuring sensors are placed where they reflect the actual ambient conditions. Communication mapping (eg, radio mapping) is also advised in order to guarantee efficient data transmission between the data loggers and the gateway. Expert support services during installation can provide onsite mapping and placement planning, from the project phase through to commissioning. This includes determining optimal sensor and gateway locations and selecting the right mix of wired and wireless solutions to match local conditions and IT security requirements. The result is a monitoring set-up that works reliably from day one.

Mistake 3: Poor alarm management

An alarm is only useful if it reaches the right person in time and is acted upon. In practice, this often fails due to unclear responsibilities, especially during shift changes. If an alarm is triggered shortly before the end of a shift, it may be passed on informally to the next operator — and sometimes goes unaddressed for hours.

Outdated user interfaces make matters worse. Many systems still rely on cluttered, decades-old layouts that obscure critical information. Modern, well-structured dashboards shorten training times and help staff react faster. Visual tools, such as traffic-light indicators, provide an immediate overview of system status and highlight where action is required.

Relying solely on email notifications is another weak point. Inboxes in industrial environments are often overloaded, and alarm messages can easily get buried. Direct alerts — for example via SMS or push notifications — ensure that critical warnings stand out and reach the right person without delay.

An effective monitoring system combines a clear, intuitive interface that makes alarms easy to recognise with flexible notification options adapted to site requirements. Depending on the set-up, alerts can be shown via visual indicators, sent as emails, or delivered directly to mobile devices through SMS-based or push notifications — ensuring they are noticed and acted upon immediately.

Mistake 4: Missing system integration

With the rise of system automation, production facilities have evolved into a patchwork of isolated solutions. A facility nowadays will typically have a building management system, an inventory management system, and an environmental parameter monitoring system, among other things. Each works independently, with different interfaces and even different data formats. This fragmentation creates silos as there is no data integration between systems.

Now the opportunity arises to fully exploit stored data by choosing systems capable of sharing data among themselves or exporting data to other platform (eg, using APIs or webhooks). An integrated monitoring landscape offers a clear advantage. It gives the possibility to visualise all parameters in one place (eg, PowerBI), improving cost efficiency while cooling a room by sending pre-alarm warnings to the building management system to control HVAC accordingly, or combining inventory data (eg, from an inventory management system) with monitored storage conditions, further improving product quality assurance.

Choose a monitoring system with integration in mind. Modern platforms offer webhooks and APIs to connect with existing infrastructure, as well as tools that visualise all relevant parameters at a glance. The result is full transparency, faster decision-making, and greater efficiency in maintaining process stability.

Mistake 5: Ignoring data redundancy

For industrial production environments, data redundancy can be a decisive factor when choosing a monitoring system. Relying solely on direct data storage on a server leaves a facility dependent on a continuous internet connection. If that link fails, so does the data flow — and in the worst case, critical records are lost entirely.

The consequences can be costly. Imagine a long-running test in product development or emissions analysis. If the internet connection drops midway and the system has no backup, hours of data may be gone. Without complete records, the test results cannot be validated, and the entire process has to be repeated. In industries with strict quality requirements — from battery production to cosmetics manufacturing — such interruptions can mean missed deadlines, wasted resources, and compliance risks.

The solution to this challenge is simple: redundancy. Robust redundancy strategies eliminate this risk by ensuring data is captured and stored at multiple points. Even if one system layer fails, the information remains safe and accessible.

This includes buffering data locally at the logger, storing it in a base unit independent of the server, and maintaining a secure central database. Alarm redundancy is equally important, making sure alerts are sent even if the primary channel is unavailable.

Advanced monitoring systems are designed with triple data redundancy: data is stored on the data logger, on the base unit and on the server. This approach can support both cloud and self-hosted (on-premise) monitoring systems, especially in facilities with strict data security policies, ensuring that no information is lost, and that monitoring remains uninterrupted under any network conditions.

Conclusion

In industrial production, effective temperature and humidity monitoring is more than just installing sensors. It requires regular calibration, correct placement, clear alarm processes, integrated systems and robust data redundancy. Addressing these points not only protects product quality and compliance but also reduces downtime and costly rework.

<sup>1. International Electrotechnical Commission 2008, IEC 60751: Industrial platinum resistance thermometers and platinum temperature sensors, Section 5.4.
2. Mangum BW, Furukawa, GT 1984, ‘Stability of Small Industrial Platinum Resistance Thermometers’, Journal of Research of the National Institute of Standards and Technology, vol 89, no 6, pp 795–801.
3. Kowal D, Nwaboh, J et al. 2020, ‘Long-term stability of meteorological temperature sensors’, Meteorological Applications, vol 27, issue 5 2020, pp 12–15.</sup>

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