Predictive Maintenance AI for 500 Induction Motors at a Motorcycle Manufacturing Plant
Predictive Maintenance AI for 500 Induction Motors at a Motorcycle Manufacturing Plant
Predictive Maintenance AI for 500 Induction Motors at a Motorcycle Manufacturing Plant
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
PredictAI·Motor · Manufacturing · Predictive AI + Industrial Automation
PredictAI·Motor · Manufacturing · Predictive AI + Industrial Automation
PredictAI·Motor · Manufacturing · Predictive AI + Industrial Automation
PredictAI·Motor · Manufacturing · Predictive AI + Industrial Automation
Client
Leading Oil & Gas Company
Industry
TAGS
Predictive AI · Manufacturing · Industrial Automation · ML
Technologies
Current Signature Analysis · Leakage Current Sensors · Python · ML Models · Industrial IoT · Real-Time Analytics · Predictive Maintenance Platform
Engagement
Lumen™️ → Forge™️
Client Overview
One of the largest motorcycle manufacturing plants in the world — 1.5 million units per year production capacity, 8,000 workers across multiple shifts, 500+ induction motors distributed across machines on the factory floor. Each machine runs 3–7 induction motors. Planned maintenance happened once a month, stopping production for an entire day.
Results:
500+
induction motors monitored across the factory floor
The Challenge
Unexpected induction motor breakdown during production caused immediate line stoppages — at 1.5M units/year output, every unplanned hour of downtime translated to significant revenue loss. Planned monthly maintenance was a blunt instrument — shutting down the entire factory floor regardless of actual motor condition. The client had no visibility into which motors were approaching failure and which had months of healthy operation remaining. They needed a system that could detect impending failure with enough lead time to plan replacement without stopping the line.
The Solution
Palpx designed a minimally invasive fault detection system using leakage current analysis and current signature analysis — no physical modification to motors required, sensors clipped onto existing electrical connections. The ML model was trained on healthy and degraded motor signatures across all motor types in the plant. Advanced analytics provided early warning scores per motor, identified common failure patterns across machine types and enabled efficient replacement procurement scheduling. The system moved the client from reactive breakdown response and blanket scheduled maintenance to condition-based maintenance — replacing only motors showing genuine degradation signals.
Client
Leading Oil & Gas Company
Industry
TAGS
Predictive AI · Manufacturing · Industrial Automation · ML
Technologies
Current Signature Analysis · Leakage Current Sensors · Python · ML Models · Industrial IoT · Real-Time Analytics · Predictive Maintenance Platform
Engagement
Lumen™️ → Forge™️
Client Overview
One of the largest motorcycle manufacturing plants in the world — 1.5 million units per year production capacity, 8,000 workers across multiple shifts, 500+ induction motors distributed across machines on the factory floor. Each machine runs 3–7 induction motors. Planned maintenance happened once a month, stopping production for an entire day.
Results:
500+
induction motors monitored across the factory floor
The Challenges
Unexpected induction motor breakdown during production caused immediate line stoppages — at 1.5M units/year output, every unplanned hour of downtime translated to significant revenue loss. Planned monthly maintenance was a blunt instrument — shutting down the entire factory floor regardless of actual motor condition. The client had no visibility into which motors were approaching failure and which had months of healthy operation remaining. They needed a system that could detect impending failure with enough lead time to plan replacement without stopping the line.
The Solution
Palpx designed a minimally invasive fault detection system using leakage current analysis and current signature analysis — no physical modification to motors required, sensors clipped onto existing electrical connections. The ML model was trained on healthy and degraded motor signatures across all motor types in the plant. Advanced analytics provided early warning scores per motor, identified common failure patterns across machine types and enabled efficient replacement procurement scheduling. The system moved the client from reactive breakdown response and blanket scheduled maintenance to condition-based maintenance — replacing only motors showing genuine degradation signals.
PredictAI·Motor · Manufacturing · Predictive AI + Industrial Automation
Client
Leading Oil & Gas Company
Industry
TAGS
Predictive AI · Manufacturing · Industrial Automation · ML
Technologies
Current Signature Analysis · Leakage Current Sensors · Python · ML Models · Industrial IoT · Real-Time Analytics · Predictive Maintenance Platform
Engagement
Lumen™️ → Forge™️
Client Overview
One of the largest motorcycle manufacturing plants in the world — 1.5 million units per year production capacity, 8,000 workers across multiple shifts, 500+ induction motors distributed across machines on the factory floor. Each machine runs 3–7 induction motors. Planned maintenance happened once a month, stopping production for an entire day.
Results:
500+
induction motors monitored across the factory floor
The Challenges
Unexpected induction motor breakdown during production caused immediate line stoppages — at 1.5M units/year output, every unplanned hour of downtime translated to significant revenue loss. Planned monthly maintenance was a blunt instrument — shutting down the entire factory floor regardless of actual motor condition. The client had no visibility into which motors were approaching failure and which had months of healthy operation remaining. They needed a system that could detect impending failure with enough lead time to plan replacement without stopping the line.
The Solution
Palpx designed a minimally invasive fault detection system using leakage current analysis and current signature analysis — no physical modification to motors required, sensors clipped onto existing electrical connections. The ML model was trained on healthy and degraded motor signatures across all motor types in the plant. Advanced analytics provided early warning scores per motor, identified common failure patterns across machine types and enabled efficient replacement procurement scheduling. The system moved the client from reactive breakdown response and blanket scheduled maintenance to condition-based maintenance — replacing only motors showing genuine degradation signals.