When is predictive maintenance worthwhile?

Predictive maintenance is worthwhile if you have critical systems, want to reduce high maintenance costs, have access to relevant data and want to extend the service life of your systems. It can also be useful in safety-critical industries and for creating competitive advantages. A precise cost-benefit analysis is advisable.

How is data collected for predictive maintenance?

Data for predictive maintenance is collected by sensors, IoT devices and machine monitoring systems. These collect information such as temperature, vibrations, pressure and more to monitor the condition of the equipment. The collected data is then analysed to predict potential problems and maintenance needs.

What is the difference between predictive maintenance and preventative maintenance?

Predictive maintenance uses data analytics and sensors to predict maintenance needs, while preventative maintenance is based on predetermined schedules to perform preventative maintenance, regardless of the current condition of the equipment. Predictive maintenance is data-driven and can be more cost-efficient.

How is AI used in predictive maintenance?

In predictive maintenance, AI agents are used to optimise the maintenance and monitoring of industrial systems. This is mainly done through three key processes: Data acquisition and processing: AI agents continuously collect data from various sensors and systems that indicate the physical and operational conditions of the machines. This includes vibration data, temperature measurements, energy consumption and other operating parameters. This data is pre-processed to reduce noise and extract relevant features. Pattern recognition and modelling: AI uses machine learning (ML) techniques, specifically supervised and unsupervised learning methods, to recognise patterns from this data. Using historical data in which known machine failures and their signs are documented, the AI agent trains models to evaluate the current state of a machine. Using methods such as regression, classification or anomaly detection, the AI agent learns to identify early warning signals for potential failures or maintenance requirements. Prediction and decision-making: Based on the recognised patterns and the trained model, the AI agent can make predictions about the future condition and maintenance needs of the machines. These predictions enable the maintenance teams to act proactively instead of reacting to failures. The AI can also provide recommendations for optimal maintenance intervals and procedures to maximise operational efficiency and minimise downtime risks. In addition, advanced AI systems in predictive maintenance often also integrate feedback loops in which the results of the maintenance measures carried out contribute to the continuous improvement of the predictive models. This leads to constant optimisation and adaptation to changing operating conditions and machine states.

Preventive & Predictive Maintenance 4.0:
Proactive maintenance of machines and systems
and reduction of downtime

Predictive maintenance in Industry 4.0

Predictive Maintenance:
AI agent-driven progress in Industry 4.0

Predictive maintenance is more than just a trend - it is a revolution in Industry 4.0, fuelled by AI agents. These intelligent systems enable machine operators to not only prevent breakdowns and downtimes of their systems, but to strategically predict and control them.

The use of AI agents in real-time data monitoring takes predictive maintenance to a new level. Sensors and monitoring systems collect machine data that is analyzed by AI agents. These intelligent systems use advanced algorithms such as machine learning (ML) and artificial intelligence (AI) to make precise predictions about the condition of the machines. They identify anomalies and potential problems at an early stage, allowing preventive maintenance measures to be initiated in a targeted manner.

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The advantages of AI-controlled predictive maintenance:

Targeted and timely maintenance measures

Thanks to the analysis of machine data, maintenance work can be planned precisely and in good time. This prevents unnecessary downtime and expensive repairs.

Improved system availability

Potential problems are recognised at an early stage, which increases the availability of the systems. Production can continue continuously and without interruptions.

Increasing efficiency and productivity

Minimising downtime and costly production stoppages increases production efficiency. The productivity of the systems is maximised.

Cost reduction

Predictive maintenance significantly reduces the total cost of ownership by avoiding expensive emergency repairs and making maintenance more efficient.

Proactive and data-driven

Compared to reactive or preventive maintenance, predictive maintenance is characterised by its proactive nature. It is based on the analysis of real-time data and enables needs-based maintenance.

Quality improvement

Guarantee consistently high product quality thanks to stable machine performance. Production errors and rejects are minimised.

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elunic AG: Your service provider
for Predictive Maintenance

Accelerate digitalisation significantly.

6 steps to a successful predictive maintenance project

Define Project Goal

Increasing uptime or optimizing spare part logistics are some examples of possible project goals.

Identifying Stakeholders

Be it shop floor workers, IT or the workers' council: a strategic project, such as predictive maintenance, is based on the cooperation and participation of many parties.

Define Intermediate Goals

Introducing predictive maintenance takes time. Intermediate steps, like the implementation of condition monitoring, allow companies to benefit from the increased level of digitization at an early stage.

Retrofitting Sensors

Often, existing sensors can be used. If no sensors are available, legacy equipment can be retrofitted at low costs.

Ensure Data Transmission

Sensor data are to be transmitted electronically to a central IT system. For this purpose, suitable transmission channels must be set up.

Clarify Data Retention and Operation

A coherent operating concept is required for the vast amount of data that has to be collected. elunic offers its customers proven standard solutions to accomplish this task.

Frequently asked questions

When is predictive maintenance worthwhile?
Predictive maintenance is worthwhile if you have critical systems, want to reduce high maintenance costs, have access to relevant data and want to extend the service life of your systems. It can also be useful in safety-critical industries and for creating competitive advantages. A precise cost-benefit analysis is advisable.
How is data collected for predictive maintenance?

Data for predictive maintenance is collected by sensors, IoT devices and machine monitoring systems. These collect information such as temperature, vibrations, pressure and more to monitor the condition of the equipment. The collected data is then analysed to predict potential problems and maintenance needs.
What is the difference between predictive maintenance and preventative maintenance?
Predictive maintenance uses data analytics and sensors to predict maintenance needs, while preventative maintenance is based on predetermined schedules to perform preventative maintenance, regardless of the current condition of the equipment. Predictive maintenance is data-driven and can be more cost-efficient.
How is AI used in predictive maintenance?
In predictive maintenance, AI agents are used to optimise the maintenance and monitoring of industrial systems. This is mainly done through three key processes:
1. Data acquisition and processingAI agents continuously collect data from various sensors and systems that indicate the physical and operational conditions of the machines. This includes vibration data, temperature measurements, energy consumption and other operating parameters. This data is pre-processed to reduce noise and extract relevant features.
2. Pattern recognition and modellingAI uses machine learning (ML) techniques, in particular supervised and unsupervised learning methods, to recognise patterns from this data. Using historical data in which known machine failures and their signs are documented, the AI agent trains models to evaluate the current state of a machine. Using methods such as regression, classification or anomaly detection, the AI agent learns to identify early warning signals for potential failures or maintenance requirements.
3. Forecasting and decision-makingBased on the recognised patterns and the trained model, the AI agent can make predictions about the future condition and maintenance requirements of the machines. These predictions enable the maintenance teams to act proactively instead of reacting to failures. The AI can also provide recommendations for optimal maintenance intervals and procedures to maximise operational efficiency and minimise downtime risks.
In addition, advanced AI systems in predictive maintenance often also integrate feedback loops in which the results of the maintenance measures carried out contribute to the continuous improvement of the prediction models. This leads to constant optimisation and adaptation to changing operating conditions and machine states.