Energy-Centered Predictive Maintenance for Food & Beverage

Trusted by manufacturers across the sector, our platform helps reduce equipment failures, ensure consistent product quality, and minimize the environmental impact of sudden shutdowns.

Preventing Contamination through Human Intervention

Adhering to stringent quality and health norms, coupled with maintaining rigorous safety standards, is a paramount challenge in the food and beverage industry. Ensuring that human intervention, including data collection methods to monitor equipment condition aligns seamlessly with these standards becomes a critical aspect of preventing contamination risks.

Unplanned Downtime and Batch Loss

Unplanned downtime poses a significant challenge in the food and beverage industry, potentially resulting in lost batches and disrupting carefully orchestrated production schedules. This critical issue demands a proactive approach that can mitigate the impact of unforeseen issues and enhance the overall operational resilience of manufacturing processes.

Manual Data Analysis

The reliance on manual data analysis, particularly in tasks like vibration analysis, presents a notable challenge for the food and beverage industry. This process demands a high level of expertise and experienced maintenance teams to accurately interpret the collected data. Moreover, the associated costs of vibration monitoring equipment, coupled with the necessity for regular maintenance and calibration, contribute to an increased overall cost of upkeep. Addressing this challenge is crucial for optimizing maintenance processes and achieving cost efficiency in the industry.

Remote Condition Monitoring

We bring the power of Remote Monitoring with IoT & AI to the forefront, eliminating the need for human intervention in routine monitoring processes. This technology ensures continuous equipment monitoring, providing real-time insights into the health and performance of your machinery, even in remote locations.

Automated Anomaly Detection

Our solution employs Automated Anomaly Detection to proactively prevent unplanned downtime. It analyses machine health data by using a combination of AI and physics-based models, to identify subtle changes in machine operations at an early stage, giving your team more time to plan and react.

Automated Prediction & Prescription

This feature enables early fault detection and provides automated recommendations for corrective actions. By identifying patterns, and offering actionable insights, your maintenance team can implement timely measures, reducing downtime and optimizing operational efficiency.

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Case Study: $250,000+ in Downtime Avoided at a Leading Dairy Processing Plant with Energy-Centered Predictive Maintenance

A leading dairy product manufacturer in North America faced high risks from hidden faults in critical machines (especially with many located in remote or hard to reach areas), leading to reactive maintenance and potential production losses.

To address this, our MachineDoctorâ„¢ 6-in-1 wireless sensors were deployed across key assets, including motors, pumps, and blowers. The system provided real-time fault detection and AI-powered Remaining Useful Life (RUL) insights.

When unbalance was detected on a 30 HP motor powering a Reverse Osmosis (RO) pump, a Stage 4 fault alert was triggered. This early warning enabled timely corrective action. This prevented over 16 hours of unplanned downtime!