Predictive Maintenance System

Problem Statement

Industrial equipment such as motors, pumps, compressors, and conveyors often fail without warning, leading to unplanned downtime, production losses, and high maintenance costs.

Ground Reality

Across industries, maintenance practices typically suffer from:

• Reactive maintenance (repair after failure)
• Periodic maintenance without actual condition assessment
• No correlation between electrical and mechanical parameters
• Lack of real-time monitoring at machine level

Solution Approach

Hexitronics introduces a 3-sensor predictive maintenance system integrating:

• Vibration Sensor: Detects mechanical anomalies, imbalance, misalignment
• Temperature Sensor: Identifies overheating and friction-related issues
• Current Sensor: Monitors electrical load and abnormal power consumption

The system continuously analyzes combined sensor data and transmits it to the cloud for intelligent diagnostics and alerts.

Sensor Intelligence (Core Logic)

• High vibration + Normal current → Mechanical issue
• High current + Normal vibration → Electrical issue
• High temperature + Increasing vibration → Imminent failure
• All parameters deviating → Critical machine condition

System Architecture

Key Features

• Real-time multi-parameter monitoring
• Early fault detection
• Machine health scoring
• Battery / mains powered deployment
• Secure cloud integration

Future Dashboard & Analytics

Advanced analytics include:

• Machine health index
• Failure prediction alerts
• Trend analysis of vibration, temperature, and current
• Maintenance scheduling recommendations
• Historical performance insights

Benefits

• Reduction in unplanned downtime
• Lower maintenance costs
• Increased equipment lifespan
• Improved operational efficiency
• Data-driven maintenance decisions

Deployment Strategy

Step 1: Deploy sensors on critical machines
Step 2: Establish baseline machine behavior
Step 3: Enable predictive analytics and alerts
Step 4: Scale across entire plant