Machine condition monitoring makes the health of equipment visible through continuously or periodically collected measurements. This guide explains methods, sensors, thresholds, and the difference between condition monitoring and predictive maintenance. It also shows how manufacturers can start with one machine and turn reliable monitoring data into a scalable service.
What is condition monitoring?
Condition monitoring describes the continuous recording of machine status using sensors and software. The condition of a machine is monitored in real time by measuring parameters such as temperature, vibration, pressure, or power consumption using sensors; this allows wear and tear or potential failures to be detected in good time. The aim is to avoid unplanned downtime and increase the service life of the equipment.
Why is machine monitoring important?
Condition monitoring is the basis for predictive maintenance. A well-implemented system offers a wide range of advantages:
- Reduced downtime: Early warnings enable maintenance measures to be planned before unplanned downtime occurs.
- Longer machine service life: If signs of wear are detected in good time, components can be replaced or repaired in a targeted manner.
- Higher efficiency and productivity: Continuous condition data reveals bottlenecks and helps to optimize processes.
- Greater safety: Monitoring critical parameters such as temperature or vibration improves occupational safety.
- Cost savings: Maintenance work is carried out as needed, eliminating unnecessary downtime and superfluous spare parts.
Sensors and data – the heart of condition monitoring
Various types of sensors are used in condition monitoring to collect all operating data. Typical example are temperature, vibration, and pressure sensors. The sensors provide raw data, which is then analyzed and visualized. A modern condition monitoring platform combines the measured values with intelligent algorithms to identify deviations or patterns.
Typical sensor data
| Sensor type | Measured parameter | Area of application (examples) |
|---|---|---|
| Temperature sensor | Heat generation, overheating | Electric motor, bearings, gearbox |
| Vibration sensor | Vibrations, imbalance, resonance | Rotating machines, pumps |
| Pressure sensor | Pressure curve in liquids/gases | Hydraulic systems, pneumatics |
| Current sensor | Current consumption, load profiles | Drives, electric drives |
| Acoustic sensor | Noise level, anomalies | Bearings, gearboxes, fans |
| Humidity sensor | Air/material humidity | Air conditioning systems, food |
The short table helps you select the right sensor technology. In a condition monitoring system, data is collected, stored centrally, and evaluated. Values outside defined limits trigger a warning.
Which condition monitoring methods are used?
The right monitoring method depends on the machine, component, and potential failure mode. No single measurement is equally useful for every application.
- Vibration analysis: Detects imbalance, misalignment, and emerging damage in bearings or other rotating components.
- Temperature monitoring and thermography: Reveals overheating, friction, and uneven heat distribution.
- Oil analysis: Identifies wear particles, contamination, and changes in lubricant quality.
- Acoustic monitoring: Detects unusual noise or high-frequency signals that can indicate leaks or material damage.
- Electrical measurements: Current, voltage, and load profiles can reveal overload, blockage, or changes in operating conditions.
The objective is not to collect as much data as possible. Each measurement, threshold, and expected response should be selected so that a deviation leads to a specific and traceable maintenance or service action.
Practical applications
Condition monitoring is used in many industries. Examples include manufacturing, power generation, aerospace, and oil and gas. Wherever high plant availability and safety are crucial, machine monitoring helps to reduce costs and increase productivity.
A concrete example is machining equipment in the metal industry. Vibration measurement detects imbalance before chips become jammed in the tool. In power plants, temperature and pressure are monitored to protect turbines from overheating. In aerospace, sensors are used to monitor engines and control systems, while the oil and gas industry relies heavily on pressure and temperature sensors.
Practical example: From a temperature deviation to a service case
A machine sends temperature, runtime, and operating status data to the monitoring platform. A warning range is defined for a critical bearing. If the temperature exceeds the threshold for a specified period rather than for only a brief peak, the service team receives an alert together with the machine, customer, and trend data.
An expert reviews the trend and decides whether controlled continued operation is possible. If necessary, a ticket is created or an on-site technician is connected through remote support. The decision and completed steps remain documented with the service case. At the same time, the customer can receive a simplified view showing the status, warning, and agreed response.
The value therefore comes not from adding another dashboard, but from reducing the time between a deviation and a qualified action.
Condition monitoring at ADTANCE – the PVM system
ADTANCE offers PVM (Process Visualisation & Monitoring), a modular condition monitoring system. With this solution, machines and systems are continuously monitored via existing or retrofitted sensors, and the measurement data is collected and visualised centrally. Limit values and measurement curves can be defined individually, creating a reliable condition monitoring system.
Particularly noteworthy are the separate expert and customer views: while experts can see the complete machine status, customers can only view relevant diagrams and use the data for their products. The stored data is located in German data centers with ISO 27001 certification; all communication is encrypted, and the system can be operated on-premise if desired. ADTANCE PVM thus combines scalable cloud technology with maximum data security.
Predictive maintenance and modern analytics
Condition monitoring is the first step toward predictive maintenance. While condition monitoring primarily records the current status and triggers alarms, predictive maintenance analyzes historical data using artificial intelligence (AI) and machine learning. This allows trends to be identified and failures to be predicted before they occur. An example: Based on the vibration signals of a motor, an algorithm learns to recognize typical patterns for bearing damage and reports the impending failure in good time. The combination of real-time data and statistical analysis opens up new potential for cost reduction and productivity increases.
Step by step to your own condition monitoring system
- Define objectives: Determine which machines are to be monitored and which key performance indicators (KPIs) are crucial.
- Select sensors: Choose suitable sensors and interfaces to record the relevant physical variables. Take into account the environment (e.g., temperature, humidity) and the required measurement accuracy.
- Data integration: Connect the sensors to a platform such as ADTANCE PVM. The data should be stored centrally, securely encrypted, and available for analysis.
- Analysis and alerts: Set up thresholds and alerts. Use charts, dashboards, and, if necessary, AI-based analytics to identify patterns and enable predictive maintenance.
- Continuous optimization: Evaluate the results regularly. Adjust thresholds, add new sensors, and integrate insights from practice.
Typical implementation challenges
Condition monitoring projects often struggle not because sensors are missing, but because responsibilities and follow-up processes are unclear.
- Too much data: Measurements are collected without defining which deviations are actually relevant.
- Unsuitable thresholds: Limits that are too narrow create alert fatigue, while limits that are too wide detect problems too late.
- Missing machine context: A measurement only becomes meaningful when the operating state, load, and historical trend are considered.
- Unclear responsibilities: Every warning needs an owner who evaluates it and knows which response is expected.
- Disconnected systems: Monitoring, ticketing, and service documentation should exchange information so that insights do not remain isolated in a dashboard.
A limited pilot with one suitable machine reduces these risks and creates a reliable basis for scaling the monitoring program.
Conclusion
Machine condition monitoring makes changes in equipment health visible at an early stage. Its business value emerges when relevant measurements, meaningful thresholds, and a clear follow-up process work together.
ADTANCE PVM helps machine and plant manufacturers visualize existing or additional machine data, evaluate warnings, and provide selected information to customers as a digital service.
Start condition monitoring with one machine
We review the data source, threshold, and follow-up process for a limited pilot and show how it can become a scalable monitoring service.
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Frequently asked questions about condition monitoring and ADTANCE PVM
What is condition monitoring?
Condition monitoring is the continuous collection and evaluation of machine health data using sensors and software. It helps detect wear and potential failures early so that unplanned downtime can be avoided.
What are the benefits of condition monitoring?
The main benefits are fewer unplanned stoppages, longer equipment life, higher efficiency and productivity, improved safety, and lower costs through condition-based maintenance.
Which sensor data are typically captured?
Common inputs include temperature, vibration, pressure, current, acoustic, and humidity readings from equipment such as motors, bearings, pumps, gearboxes, and hydraulic systems.
Which industries use condition monitoring?
Condition monitoring is used in manufacturing, power generation, aerospace, oil and gas, and other industries where equipment availability, safety, and efficiency are critical.
How is condition monitoring different from predictive maintenance?
Condition monitoring tracks the current machine state and triggers alerts when measurements deviate from defined limits. Predictive maintenance additionally analyzes historical data with AI and machine learning to identify trends and forecast failures.
What is ADTANCE PVM and how does it work?
ADTANCE PVM (Process Visualisation & Monitoring) is a modular machine monitoring platform. It collects sensor data centrally, visualizes measurements and thresholds, and supports secure, scalable condition monitoring.
How secure is data storage with ADTANCE PVM?
Data is stored in ISO 27001-certified data centers in Germany and transferred using encryption. An on-premise deployment on company-owned servers is also available on request.
Does ADTANCE PVM offer separate views for experts and customers?
Yes. Experts can access complete condition and diagnostic data, while customers only see approved charts and KPIs relevant to their equipment.
Do I need new sensors or can I use existing ones?
Both options are possible. PVM can connect existing machine signals and can be expanded with additional sensors or an IoT gateway when required.
Can ADTANCE PVM determine initial thresholds and how quickly can we start?
PVM can calculate initial thresholds from normal machine operation. The first steps of a limited monitoring pilot can usually be implemented within a few weeks.