EdgeTick in Action
- ipsitasan1
- Mar 31
- 3 min read
Cooling System Monitoring System
Every machine tells a story, and EdgeTick listens. This is how it turned a potential disaster into a maintenance win.
The Challenge
Cooling systems play a critical role in preventing equipment from overheating—whether in full-scale industrial facilities or controlled laboratory environments. In this case study, EdgeTick was deployed in a lab-scale testbed that emulated real-world industrial conditions. This helped validate EdgeTick’s capabilities in detecting early-stage faults even in compact setups.
EdgeTick in Action: Step-by-Step Implementation
1. System Setup and Integration
The EdgeTick unit was installed and integrated into a laboratory test cooling system built with industrial-style components. The test setup included a DC motor commonly used in water filtration systems, a radiator, pump, and coolant piping. Key monitoring points—motor, pump, and coolant lines—were equipped with sensors. The layout was designed to reflect the operational dynamics of real-world machinery in a scaled-down environment. The EdgeTick unit was installed and integrated into a laboratory test cooling system built with industrial-style components. The test setup included a motor, radiator, pump, and coolant piping. Key monitoring points—motor, pump, and coolant lines—were equipped with sensors. The layout was designed to reflect the operational dynamics of real-world machinery in a scaled-down environment.
2. Smart Sensing Deployed
EdgeTick’s sensing suite captured crucial system performance data:
· INA186 Current Sensor: Measured motor power draw to monitor electrical load variations.
· ADXL345 Vibration Sensor: Captured mechanical vibrations along three axes to detect imbalance, wear, or looseness.
· YF-S402B Flow Sensor: Measured coolant flow to ensure adequate thermal regulation.
· DS18B20 Temperature Probe: Recorded system temperature to assess cooling efficiency.
These sensors provided a comprehensive overview of the system’s operational health.
3. Data Acquisition & Signal Conditioning
Raw signals were passed through a signal conditioning stage to clean and scale them appropriately. Noise was filtered out, and voltage levels were adjusted to suit the microcontroller’s ADC. This guaranteed accurate digital readings for real-time computation.
4. On-Device Data Processing & Feature Engineering
EdgeTick processed current and vibration signals on-device:
· Time-Domain Analysis: Computed statistical values such as mean, RMS, variance, and kurtosis to describe how signals changed over time.
· Frequency-Domain Analysis: Performed FFT (Fast Fourier Transform) to capture frequency content—dominant bands, harmonic patterns, and energy distribution.
These dual-domain features were grouped by likely fault conditions, enabling the system to understand the subtle differences between healthy and faulty behavior. The processing and transformation happened on-device, in under 80 milliseconds, allowing for rapid, autonomous decision-making.
5. AI Analysis with Neural Network
EdgeTick’s embedded neural network, pre-trained on a diverse dataset of normal and faulty operation scenarios, processed the extracted features. It identified a spring failure signature with high confidence. The AI inference happened on the fly with an accuracy rate of 96%, showing EdgeTick’s capability to deliver industrial-grade fault detection.
6. Real-Time Alert System and Monitoring Interface
Upon detecting the anomaly, EdgeTick pushed an alert through its web dashboard. The interface displayed live system health metrics, past trend data, and fault classifications—available remotely for immediate decision-making.
7. Proactive Maintenance Response
The alert prompted a manual inspection. The team verified a degraded spring component, confirming EdgeTick’s diagnosis. A timely replacement was carried out, and operations continued without failure, demonstrating the effectiveness of predictive maintenance in even the most compact environments.
The Result
EdgeTick successfully identified an early-stage failure using only edge-computed data from the test system. The system demonstrated:
· Sub-80 millisecond response time
· 96% fault classification accuracy
· Seamless, autonomous edge-based operation
This proved that EdgeTick can deliver industrial-grade insights even in simulated test conditions, validating its reliability and scalability for real-world deployment.
Business Benefits Delivered
· Zero Unplanned Downtime: Predictive alerts enabled uninterrupted testing.
· Cost Savings: Prevented a component failure that could have compromised the entire test cycle.
· Data-Driven Insights: Real-time dashboards delivered clarity and operational transparency.
· Scalability: EdgeTick's architecture is portable from lab to plant floor with no redesign.
EdgeTick turned whispers of a problem into a clear, actionable insight. That’s not just maintenance—it’s smart prevention.
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