- Asset health management
- Predictive maintenance with root cause
- Other predictive maintenance sensors
Maintenance sensors
Condition monitoring and predictive maintenance are proactive strategies to optimize equipment performance and prevent unplanned downtime. Condition monitoring tracks and analyzes equipment parameters in real-time to detect early signs of wear or failure, enabling timely intervention. Predictive maintenance takes this further by using historical data, machine learning algorithms, and analytics to forecast future failures, allowing for scheduled maintenance and avoiding unexpected downtime.
The ifm product range includes solutions for monitoring vibration, oil quality, temperature, pressure, and consumption of compressed air and water. Combining predictive maintenance with condition monitoring helps extend equipment lifespan and reduce maintenance costs.
Vibration
Gain valuable insights into the health of your machinery with the VVB3 condition monitoring sensor. The VVB3 sensor continuously detects vibration in three axes and additionally measures the surface temperature of non-rotating components.
Learn more about ifm's 3-axis IO-Link vibration sensor for smart asset health monitoring.
Temperature
Temperature monitoring is critical for asset health and predictive maintenance, providing insights into machine conditions, preventing overheating, and identifying early signs of wear or malfunction. Consistent temperature tracking helps avoid costly downtime and extend equipment life.
ifm’s robust temperature sensors, featuring advanced RTD technology and hermetically sealed stainless steel designs, offer precise, reliable readings in harsh environments. With IO-Link connectivity, these sensors enable real-time monitoring, easy trend analysis, and seamless integration into existing systems, supporting proactive maintenance and optimizing operational efficiency.
Systems for oil quality
Oil quality monitoring systems play a key role in identifying contaminants like water or abrasion particles in hydraulic fluids, preventing damage to critical components such as pumps, valves, cylinders, and hydraulic power units.
Oil humidity
ifm's LDH1 series measures the relative moisture content in machine oils in real time.
Excess moisture in lubricating systems rapidly degrades bearing surfaces, leading to failure and secondary damage. In hydraulic systems, moisture can damage high-pressure surfaces on rotors, pistons, and poppets. By monitoring moisture content in oils, you can prevent corrosive effects and avoid costly equipment damage.
Particle counter
The LDP particle counter sensor monitors the effectiveness of filtration systems and equipment wear.
Detecting particles in oil is the most effective real-time method to protect equipment. The sensor optically measures particle size and quantity according to standard oil quality specifications, ensuring optimal filtration and early detection of wear.
Pressure
Pressure monitoring provides critical insights into the performance and integrity of industrial systems. By tracking pressure changes in gasses and liquids, pressure sensors help detect early signs of equipment wear, leaks, or system malfunctions. Monitoring pressure can prevent costly failures by identifying issues like excessive pressure peaks, which can damage components such as valves and pumps. ifm's advanced pressure sensors offer high overload resistance, ensuring reliable operation even under extreme conditions. With features like IO-Link connectivity, these sensors enable real-time data collection and analysis, supporting proactive maintenance strategies and optimizing system efficiency.
Flow
Accurate flow measurements help detect issues like leaks, blockages, or irregular flow rates that can lead to equipment wear and costly downtime. With applications ranging from monitoring coolants and oils to gases like compressed air and nitrogen, flow sensors provide critical data for maintaining efficient operations. ifm's advanced flow sensors, equipped with IO-Link technology, allow for real-time data collection, trend analysis, and seamless integration into existing systems. This data enables proactive maintenance, helping to identify potential issues before they result in unplanned failures and extending the lifespan of equipment.