You can upgrade a wide range of devices, including but not limited to:

• Industrial machinery such as pumps, motors, compressors, and turbines.
• Manufacturing equipment like CNC machines, conveyors, and other industrial robotics.
• HVAC systems, including chillers, boilers, and air-handling units.
• Transportation assets like vehicles, aircraft, and trains.
• Power generation equipment such as generators and turbines.
• Medical devices and equipment in healthcare settings.

Yes, it can complement preventive and corrective approaches by providing advanced insights into equipment conditions.

PdM solutions harness the power of predictive analytics, sensor data, and machine learning algorithms to assess equipment conditions in real-time. Additionally, these solutions optimise maintenance schedules, minimise unplanned downtime, and significantly enhance overall equipment reliability by leveraging historical data, advanced algorithms and remote monitoring for predictive maintenance.

• Early fault detection: AI algorithms can analyse sensor data in real-time to identify anomalies that may reveal potential equipment failures or performance issues.
• Improved accuracy and precision: AI models can continuously learn and adapt based on historical data, enabling more accurate predictions of equipment conditions and failure probabilities.
• Cost savings and increased efficiency: by identifying maintenance needs in advance, AI-based predictive maintenance avoids unnecessary actions and optimises resource allocation.
• Enhanced equipment lifespan: proactive maintenance enabled by AI-based predictive analytics can extend the lifespan of critical equipment. By addressing issues before they escalate, manufacturers can minimise wear and tear, optimise maintenance interventions, and maximise the lifespan of their assets.

Predictive maintenance predicts potential equipment failures by analysing historical and real-time data to indicate when maintenance should be performed.

On the other hand, prescriptive maintenance takes it further by recommending specific actions to optimise decisions. These strategies consider multiple factors, such as equipment health, operational context, cost factors, and business objectives, to provide actionable recommendations.

By combining these two approaches, organisations can proactively detect potential issues, accurately assess the severity, and make data-driven decisions to optimise maintenance activities and maximise overall equipment performance.

These applications can be applied in various scenarios, including:

• Optimal scheduling of maintenance tasks based on equipment health, criticality, and operational requirements.
• Recommending specific repair actions or replacement parts based on failure probabilities, cost factors, and availability.
• Providing real-time guidance to technicians, including step-by-step instructions, troubleshooting tips, and safety guidelines.
• Optimising maintenance interventions by considering factors such as weather conditions, production demand, and resource availability.
• Utilising advanced analytics and historical data to identify recurring failures' root causes and suggest long-term corrective actions.
• Analysing operational data to optimise equipment settings and parameters for improved performance and energy efficiency.
• Offering predictive maintenance insights integrated with asset management systems to enable seamless planning, tracking, and reporting of maintenance activities.