Predictive Maintenance with PRTG

Predictive maintenance is a proactive maintenance strategy that uses data analysis and predictive modeling techniques to predict when equipment or machinery is likely to fail. By monitoring various parameters and indicators, such as temperature, vibration, pressure, or performance metrics, predictive maintenance aims to detect potential issues and address them before they cause a breakdown or failure.

If you want to get more deep-dive information on predictive maintenance, read our IT Explained article.

There are several types of predictive maintenance techniques that organizations can employ based on their specific needs and the nature of their equipment. Here are some common types of predictive maintenance:

• Vibration analysis: Vibration analysis involves monitoring the vibration patterns of equipment to identify potential issues. Changes in vibration levels, frequencies, or patterns can indicate faults such as misalignment, bearing wear, or imbalance.
• Thermography: Thermography, also known as infrared thermography, uses thermal imaging cameras to detect abnormal heat patterns in equipment. It helps identify overheating components, loose connections, or insulation problems, which can be early signs of failure.
• Oil analysis: By analyzing the properties and contaminants in lubricating oils, oil analysis can provide insights into the condition of components such as engines, gearboxes, or hydraulic systems. It helps detect wear, contamination, or degradation of oil, providing early indications of potential failures.
• Acoustic monitoring: Acoustic monitoring involves analyzing the sound or noise emitted by equipment. Unusual noises, such as excessive friction, grinding, or vibrations, can indicate mechanical issues or impending failures.
• Performance monitoring: Performance monitoring involves tracking and analyzing key performance indicators (KPIs) of equipment, such as pressure, temperature, flow rate, or power consumption. Deviations from normal operating parameters can indicate impending failures or deteriorating performance.
• Condition monitoring: Condition monitoring involves continuously monitoring the health and performance of equipment through various sensors and measurements. It combines multiple techniques, such as vibration analysis, temperature monitoring, and oil analysis, to provide a holistic view of equipment condition.

In industrial IT and IIoT, the so-called maintenance maturity model provides guidelines for organizations to move from reactive maintenance strategies toward data-driven proactive maintenance strategies. The reason? Reactive strategies cost more. Each step up in the maintenance maturity model brings its own time and cost savings.

• Reactive maintenance: Equipment is only repaired or serviced after a malfunction or issue.
• Preventative maintenance: Services, repairs, and maintenance on equipment are scheduled in advance. This might prevent failures but can result in unnecessary costs and downtime if equipment does not need to be serviced.
• Condition-based maintenance: The current state of equipment is monitored and compared to past trends and patterns. When the status is outside of the normal range, maintenance is scheduled.
• Predictive maintenance: Production data is analyzed by means of AI or machine learning to predict when equipment needs servicing or maintenance.

Predictive maintenance software refers to computer-based applications or platforms that facilitate the implementation and management of predictive maintenance programs. These software solutions leverage data analysis, machine learning, and advanced algorithms to monitor equipment health, detect anomalies, and predict failures.

Predictive maintenance software can be tailored to specific industries, equipment types, or maintenance strategies. It enables organizations to implement and optimize their predictive maintenance strategies effectively, enhance equipment reliability, and reduce maintenance costs by moving from reactive to proactive maintenance practices.

Predictive maintenance and monitoring tools are related but distinct concepts in the realm of maintenance and asset management. While both involve the use of technology and data analysis, there are key differences between them.

Predictive maintenance is a comprehensive strategy that involves data analysis, modeling, and proactive maintenance planning based on predicted failures.

Monitoring tools, on the other hand, focus on real-time or periodic data monitoring to provide visibility into equipment health and performance for immediate or near-term actions. While predictive maintenance can utilize monitoring tools as a component of its overall approach, monitoring tools themselves do not necessarily incorporate the predictive modeling and analysis aspects of predictive maintenance.

In PRTG, “sensors” are the basic monitoring elements. One sensor usually monitors one measured value in your network, for example the traffic of a switch port, the CPU load of a server, or the free space on a disk drive. On average, you need about 5-10 sensors per device or one sensor per switch port.