Predictive Maintenance Solutions Powered by Artificial Intelligence

What is AI based Predictive Maintenance?

Engineer controlling industrial machine using a tablet with control panel interface

Predictive maintenance uses real-time machine data and AI models to forecast failures before they stop the line, unlike preventive maintenance based on fixed schedules. Deployed as part of a wider digital manufacturing strategy, it cuts maintenance costs by up to 15% and reduces machine failure rate by up to 15%

Deliver actionable insights in manufacturing operations by harnessing sensor data. This approach empowers classical reactive and preventive maintenance models with AI-based machine learning technology that leverages historical assets and process data to make more accurate and intelligent decisions.

Predictive vs preventive maintenance: what is the difference?

Preventive maintenance follows a fixed schedule regardless of machine condition. Predictive maintenance reacts to the actual condition of the machine, using sensor data and AI models, which cuts both unnecessary service work and unexpected breakdowns. In practice, most manufacturers run both: preventive maintenance covers routine tasks, while predictive maintenance protects the assets where downtime costs the most.

	Preventive maintenance	Predictive maintenance
Trigger	Fixed schedule (calendar or usage hours)	Actual machine condition
Data used	Manufacturer recommendations, service history	Real-time sensor data, historical process data, AI models
Servicing	Performed whether needed or not	Performed only when indicators show a developing failure
Downtime	Planned stops, but breakdowns still occur between services	Unplanned breakdowns predicted and prevented
Cost profile	Over-servicing, spare parts consumed on schedule	Lower service workload, parts replaced based on condition
Best for	Low-cost assets, routine tasks	Critical assets where downtime is expensive

Business challenges

High maintenance costs

Maintenance activities consume a significant portion of most industrial companies' budgets - labor, equipment, and spare component costs, or ongoing costs. They significantly increase operating costs and reduce profit margins if they are not managed effectively.

Lack of profound process understanding

Understanding the correlations resulting from the machines, materials, parameters, or practices used in a process must be supported by proper analytical techniques and historical data.

Machine failure and unplanned stoppages out of control

Traditional maintenance methods fail to effectively predict and prevent the occurrence of critical breakdowns on production lines, which can result in uncontrolled increases in financial and time costs, but also in risks to worker safety.

Strive to maximize the efficiency of machines on the production line

Unexpected failures on the production lines are a consequence of a lack of sufficient manufacturing process analysis. Analyze and control real-time data to monitor the machinery's performance, optimize and improve efficiency, and consequently predict and prevent asset failures.

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Features

Smarter maintenance decisions

Machine learning algorithms analyse data from all connected sources, identify anomalies and predict failures before they occur. Your maintenance teams get clear, real-time guidance on what to service and when.

Trends & correlation

Identify how process parameters affect asset health. AI models detect correlations across machines, materials and settings that are invisible in manual analysis.

Seamless integration

Connect the solution to your existing systems, from PLCs and SCADA to MES and historians. No rip-and-replace: predictive models work on the data infrastructure you already have.

Real-Time Monitoring & Alerts

Monitor machine performance on the shop floor continuously. Automated alerts show the remaining useful life of each asset, so teams act on early warnings instead of breakdowns.

Prevent equipment failures

Define critical points on the production line and get real-time recommendations before issues escalate into downtime. Service windows are planned around actual machine condition, not guesswork.

How AI and machine learning improve maintenance work

Increased OEE and equipment lifetime

Maximized Equipment Uptime

Reduced maintenance costs

Better Spare parts inventory planning

Avoid and minimise unplanned downtime

Improved workplace safety

Screenshots

Industrial Analytics platform screen analysing sensor and machine data to detect abnormal equipment behaviour

Predictive maintenance dashboard showing early detection of developing machine failures on the production line

Anomaly detection view highlighting sensor signal deviations that indicate a developing machine failure

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Take proactive steps to effectively predict issues and save costs

This powerful tool helps you reduce downtime and maintenance issues by providing AI based predictive maintenance insights and machine learning predictive analytics.

It keeps an eye on maintenance operations, estimates asset performance, and ensures equipment effectiveness with advanced analytics.

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Frequently asked questions

Predictive maintenance is a maintenance strategy that uses real-time sensor data, historical process data and AI models to forecast equipment failures before they happen. Instead of servicing machines on a fixed schedule, maintenance teams act only when the data indicates a developing problem.

AI-Based Predictive Maintenance Solutions for Manufacturing

What is AI based Predictive Maintenance?

Predictive vs preventive maintenance: what is the difference?