Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen. Predictive maintenance uses condition-monitoring equipment to evaluate an asset’s performance in real-time for optimal use of machines. A key element in this process is the Internet of Things (IoT). IoT allows for different assets and systems to connect, work together, and share, analyze and action data. There are three facets of condition monitoring: online, periodic and remote.

Once your equipment is connected, you need to start analyzing its parameters for failure. Here are some of the parameters that are commonly monitored and analyzed:

• Vibration: Monitoring the vibration of equipment, usually bearing vibration
• Temperature: Monitoring the temperature variation.
• Oil Levels: Measuring the variation in oil levels of equipment.
• Acoustics: Using ultrasound to detect changes in sound made by the equipment.
• Motor voltage and current: Monitoring for nuisance corona, destructive corona, arcing and tracking.

Benefits of Predictive Maintenance

When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when it is required. That is, just before failure is likely to occur. This brings several cost savings:

• Minimizing the time the equipment is being maintained.
• Minimizing the production hours lost to maintenance.
• Minimizing the cost of spare parts and supplies.

Why is predictive maintenance important?

Fixing something before it breaks is more efficient and cost-effective than fixing it after it breaks. It helps…

• Avoid downtime and improve productivity
• Extend the life of ~/Content/assets and defer new purchases
• Reduce the cost and complexity of repairs
• Mitigate additional or related damage
• Meet regulatory standards and compliance
• Manage spare parts, materials and inventory
• And ultimately, boost the bottom line

Levels to Achieve Digital Transformation

Predictive maintenance could be seen as a competitive advantage for oil and gas companies as well as the related service businesses, particularly during times of a recession when organizations are forced to find ways to work more efficiently and effectively. Of course, predictive maintenance does not just pertain to the manufacturing, rail, and oil and gas industries. In other applications, PdM is used to:

• Help prevent utility outages with the help of drones and sensors that map utility networks
• Detect a temperature decline in a steam pipeline, indicating a potential pressure leak
• Capture increased temperatures in electrical panels to prevent component failures
• Measure supply-side and demand-side power at a common coupling point for monitoring power consumption)
• Locate overloads in electrical panels
• Identify motor amperage spikes or overheating from bad bearings or insultation breakdowns
• Find three-phase power imbalances from harmonic distortion, overloads, degradation or failure of one or more phases