Predictive Maintenance

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WHAT IS PREDICTIVE MAINTENANCE AND CONDITION MONITORING

Production in a company can come to a complete standstill due to machine failures.

Of course, it is necessary to periodically shut down machines for maintenance or calibration to ensure accuracy, safety and efficiency.

However, when unplanned downtime occurs, it causes disruption to work processes, inefficiency, high costs and wasted time. 

Modern industrial AI software for predictive maintenance like IconPro APOLLO can solve these problems to maximize productivity.

Solutions for condition monitoring and predictive maintenance optimize machine availability.

They provide real-time alarms about critical conditions and predict machine failures before they occur to avoid unplanned downtime.

DEFINITION OF PREDICTIVE MAINTENANCE

Predictive maintenance software applies machine learning (ML) and industrial AI to machine condition monitoring data.

Corresponding industrial AI software for predictive maintenance like IconPro APOLLO anticipates when a component will fail so that preventive maintenance can be scheduled before unplanned downtime occurs.

The aim is to go beyond pure condition monitoring and to plan maintenance at an optimal and cost-efficient point in time.

In this way, machine availability is significantly maximized, but without endangering it, and a significantly higher availability and overall system effectiveness of the machines or robots is achieved. 

DEFINITION CONDITION MONITORING

Condition monitoring or the condition monitoring of machines or robots refers to the collection of data on machine or environmental conditions (e.g. vibration, temperature, etc.).

The status data collected is relevant to the availability, performance or potential failures of the machines.

They are stored to detect deviations or anomalies that could indicate a developing machine failure or downtime.

Condition monitoring is elementary for predictive maintenance, as it only enables the use of industrial AI software to predict downtimes via the collected data.

EXAMPLE CONDITION MONITORING FOR MEASURING MACHINES IN QUALITY ASSURANCE

Goals: Maximum operator efficiency, Minimum incorrect measurements 

• Problem: It is difficult for operators to keep track of measurement progress and errors from several machines at possibly different locations. 
• Solution: Monitor measurement progress, operational, environmental and error states at a glance for groups or all machines from anywhere in IconPro Apollo. 
• Added value: Less operator effort per machine, higher utilization and less time spent on errors. Increased overall equipment effectiveness. 

HOW PREDICTIVE MAINTENANCE WORKS

The main benefit of predictive maintenance is the ability to predict & schedule required maintenance based on the current & historical condition of the machine.

However, it is anything but easy to determine the exact status of complex machines or robots. 

Predictive maintenance can use condition monitoring to alert technicians to imminent machine failures when the following are true: 

• Condition monitoring via attached sensors or interfaces to the control system provides an adequate digital image of the machine condition over time 
• Predictive models that can process the collected data to automatically find anomalies and changes or anticipate failures (industrial AI) 
• IIoT technology (Industrial Internet of Things) enables connectivity between machines, software solutions and server infrastructure 

REALIZE CONDITION MONITORING FOR PREDICTIVE MAINTENANCE

Many sensors and components necessary for condition monitoring can be installed or retrofitted.

Electrical currents, vibrations, temperature, pressure, oil, noise levels, degree of corrosion and much more can be measured using modern and small external sensors,

be measured by using existing modern standard interfaces to the control system or by adapting to older analogue interfaces to the control system.

In most cases, the information is already there, just waiting to be extracted by experts like those at IconPro. 

ADVANTAGES OF PREDICTIVE MAINTENANCE & CONDITION MONITORING

Deciding on predictive maintenance in your production has many advantages. Some of them are: 

• Only necessary maintenance work, calibration or repair work incurs costs, since predictive maintenance recommends these measures in good time before significant disruptions or downtime occur.
• The operating time or overall system effectiveness of the machines is increased.
  
• Predictive maintenance keeps your machinery running like clockwork with less or no production disruption caused by unexpected downtime and reactive maintenance or repair work.
  
• Increased ability to plan with regard to the start and end times of production orders.
• Longer asset life as a result of optimal maintenance and repairs – predictive maintenance helps prevent actual asset damage, so you benefit from the performance of your assets for longer.
  
• Increased profitability: Although condition monitoring and predictive maintenance is an investment at first, it ultimately saves your production a lot of money by protecting and getting the most out of your most valuable assets.