Turning data into condition monitoring insight
As industrial plants move towards predictive maintenance, condition monitoring is becoming central to operational efficiency. In this Q&A, Rizwan Ahmed of ifm electronic, a global automation specialist, explores how sensor accuracy, digital communication and IIoT platforms are transforming raw data into actionable insights that reduce downtime and optimise maintenance strategies.
In modern manufacturing environments, the shift from reactive and preventive maintenance to condition-based and predictive strategies is accelerating. At the heart of this transition lies instrumentation – specifically the ability to acquire, process and interpret high-quality data from sensors monitoring critical assets such as motors, pumps, and fans.
Condition monitoring systems are no longer limited to isolated measurements. Instead, they integrate vibration, temperature, pressure, and flow data into unified platforms capable of delivering real-time diagnostics and long-term performance insights. Advances in digital communication protocols such as IO-Link, combined with edge processing and IIoT software, are enabling seamless interoperability between sensors and control systems while reducing integration complexity.
In this context, ifm discusses how its approach to condition monitoring combines robust sensor design with intelligent data handling. By embedding diagnostics at the sensor level and leveraging platforms such as Moneo, the company aims to simplify deployment while improving reliability, reducing false alarms, and enabling faster, data-driven maintenance decisions across complex industrial environments.
Q: How does ifm define condition monitoring within industrial manufacturing and what measurable benefits does it bring to plant operations?
Rizwan Ahmed: ifm defines condition monitoring as a proactive maintenance strategy that continuously monitors equipment and systems for potential faults or deviations from normal operating conditions. The most important measurable benefits it brings to plant operations is a reduction in unplanned downtime thanks to early detection of incipient faults, which allows them to be addressed before they progress to failures.
Q: Which types of sensors (vibration, temperature, pressure, flow) are most critical for early fault detection and how does ifm ensure their accuracy and reliability in harsh industrial environments?
Rizwan Ahmed: Vibration monitoring is one of the most effective early warning methods for detecting bearing wear, gear damage, unbalance and misalignment. Vibration monitoring is highlighted as essential for preventing catastrophic failures and maximising machine availability. ifm ensures accuracy and reliability of its products in harsh environments by careful design which maximises ingress protection and provides a wide operating temperature range. The materials used in ifm sensors are also chosen for their exceptional resistance to attack by solvents, cleaning agents and other chemicals.
Q: How does ifm integrate IOLink and other digital protocols into condition monitoring solutions and what advantages does this bring for system interoperability and diagnostics?
Rizwan Ahmed: ifm leverages IOLink’s standardisation to provide comprehensive diagnostic data in a consistent manner. Process, identification and diagnostic data are accessible in a uniform format, regardless of sensor type or manufacturer, through IOLink profiles and communication specifications. PLCs and higher level systems can retrieve sensor health indicators, operational states and error conditions using standardised function blocks, reducing integration complexity. IO-Link also allows manufacturer-specific extensions where necessary, enabling advanced diagnostics without compromising basic interoperability.
ifm’s use of IOLink as a standardised sensor interface – combined with protocolagnostic masters and hybrid sensor designs – creates highly interoperable condition monitoring systems. The key payoff is better diagnostics with lower integration effort, enabling predictive maintenance without locking customers into specific controllers, networks, or sensor vendors.
Q: Can you describe a real-world application where ifm’s condition monitoring system has prevented unplanned downtime or improved maintenance efficiency?
Rizwan Ahmed: One of the strongest documented examples comes from industrial fan monitoring, where ifm’s condition monitoring solution based on the company’s sensors, IOLink and Moneo IIoT software has delivered measurable improvements.
Factory ventilation and extraction fans are critical for:
- removing soldering fumes, dust, and particulates
- maintaining process quality
- protecting worker health
- preventing production stoppages
Historically, these fans had been serviced at fixed intervals, regardless of actual condition. This resulted in:
- Unnecessary maintenance on still-healthy equipment
- Unexpected failures due to undetected soiling or imbalance
- Avoidable production downtime when fans stopped unexpectedly
ifm implemented continuous fan monitoring using:
- Smart sensors: Sensor data (vibration, temperature, airflow, etc.) is collected directly on the fan.
- IOLink connectivity: Digital, high-quality data enables real-time condition analysis.
- Moneo IIoT platform: Moneo continuously evaluates the sensor data, detects anomalies and triggers early warnings – long before failure. It also supports:
- Alarm management
- Maintenance planning
- Documentation for recurring issues
The customer reported quantifiable, real-world benefits:
- 4.5 hours of prevented production downtime
- Early detection of fan degradation allowed action before failure occurred, saving 6.5 hours of labour
- Simple setup and automated monitoring significantly reduced manual inspection time.
- €5,000 in cost savings which came from avoiding complete fan damage and preventing production stoppages.
- Increased maintenance efficiency: Maintenance was performed only when needed – not by fixed schedules – resulting in reduced costs and optimised downtime planning.
This fan monitoring case demonstrates how ifm’s condition monitoring system achieves:
- Early fault detection (soiling, imbalance, wear)
- Reduction in unexpected failures
- Shift from preventive to condition-based maintenance
- Lower cost of ownership and downtime
- Straightforward deployment with minimal specialist involvement
These outcomes are representative of the broader value seen across pumps, motors, compressors, and other rotating assets where ifm solutions are deployed.
Q: How do your sensors and diagnostic tools handle variable operational conditions, such as fluctuating loads, temperature swings, or high-speed machinery?
Rizwan Ahmed: Industrial equipment often operates with changing mechanical loads, rotational speeds, and dynamic forces. ifm designs its sensors specifically to maintain accuracy and reliability under these conditions. For example, our VVB3 vibration sensor provides threeaxis vibration measurement and additional surface temperature data, enabling highly accurate detection of changes in machine behaviour even when loads fluctuate rapidly. It takes into account machine speed especially where this is variable and can therefore track failure modes such as unbalance and bearing defects more accurately.
Q: In multi-asset or complex industrial systems, how does ifm’s platform aggregate and visualise sensor data for actionable insights without overwhelming plant operators?
Rizwan Ahmed: We solve the complexity challenge in multi asset industrial environments by using our Moneo IIoT platform, which is designed specifically to transform large volumes of sensor data into accessible, simple, intuitive and actionable results.
Q: What role does predictive maintenance software, such as the Moneo platform, play in translating raw sensor data into actionable maintenance decisions?
Rizwan Ahmed: Raw signals from vibration, temperature, pressure, flow, and other sensors can be noisy and overwhelming when viewed directly. Moneo applies data collection, parameterisation, filtering and preprocessing in a structured way so that only meaningful, contextualised information is surfaced.
Moneo IIoT Core bundles together essential functions that include parameterisation, data collection, visualisation, monitoring, and the conversion of raw signals into usable information. This creates a single, clean data layer from thousands of data points across multiple assets.
Q: How do you address issues like false alarms, sensor drift, or environmental interference to maintain trust in automated condition monitoring?
Rizwan Ahmed: IOLink replaces analogue outputs with 100% digital communication, eliminating analogue noise and signal degradation which are major sources of false alarms. IOLink enables sensors to report:
- Out-of-range events,
- Wrong parameter values,
- Error counters,
- Internal status.
This ensures real alarms are distinguished from transient noise.
Q: With Industry 4.0 adoption growing, how are edge computing and AI analytics being integrated into ifm’s condition monitoring solutions to support smarter, real-time decision-making?
Rizwan Ahmed:The Moneo edge Gateway collects sensor data and allows users to process and visualise it locally, without needing remote servers. This means critical decisions such as anomaly detection, threshold breaches, or warnings can be generated with minimal latency.
AI is central to turning raw monitoring data into actionable intelligence. Moneo is equipped with a built-in Industrial AI Assistant and is Cloud enabled for multi-site analytics. This means that machine data from multiple sites can be aggregated and analysed globally.
Q: Looking ahead, what trends in instrumentation and sensor technology are likely to shape the next generation of condition monitoring solutions for industrial plants?
Rizwan Ahmed:AI will dominate future industrial monitoring and control, enabling systems to track fluctuations in real time and support predictive maintenance. AI and machine learning are becoming core components of next generation instrumentation, facilitating fault detection and early anomaly recognition.