AI-Enabled Equipment Monitoring Solutions

Machine Learning Meets Current Transformers: A Smarter Way to Monitor Plants Traditional plant monitoring relies on layers of sensors—flow switches, pressure switches, vibration probes—each adding cost and complexity. But with machine learning applied to current transformer (CT) technology, one simple clamp-on sensor can recognize equipment start-ups, track runtime, and even detect early signs of failure. In this white paper, I break down: - How CT-based ML systems are easy to retrofit with no downtime. - Why one sensor can often replace multiple instruments. - How signature learning enables predictive maintenance. - The strengths and trade-offs of technologies from ABB, Siemens, Fluke, and others. For plant managers and engineers, this isn’t abstract AI—it’s a practical, economical way to improve reliability and reduce maintenance headaches.

SUCCESS! Machine monitoring is a pivotal component in modern manufacturing, enabling real-time oversight of equipment performance and operational efficiency. By collecting and analyzing data from machines, manufacturers can enhance productivity, reduce downtime, and make informed decisions that drive continuous improvement. Importance of Machine Monitoring: 1. Automated data collection eliminates manual entry errors and provides immediate insights into machine status, utilization, cycle times, and operator performance. This real-time visibility allows for prompt responses to issues, minimizing disruptions. 2. Enhanced Operational Efficiency: Monitoring systems identify bottlenecks and inefficiencies, enabling manufacturers to optimize processes, improve machine utilization, and increase overall equipment effectiveness (OEE). 3. Predictive Maintenance: By analyzing parameters like vibrations, temperature, and pressure, machine monitoring facilitates predictive maintenance strategies, reducing unplanned downtime and extending equipment lifespan. 4. Quality Assurance: Continuous monitoring ensures machines operate within specified parameters, maintaining product quality and reducing defects. This leads to higher customer satisfaction and reduced waste. MachineMetrics is a leading provider of machine monitoring solutions tailored for machine shops. Their platform offers several key benefits: • Automated Data Collection: MachineMetrics’ system seamlessly integrates with various machinery to collect data without manual intervention, ensuring accuracy and timeliness. • Real-Time Analytics: The platform provides real-time dashboards and reports, offering insights into machine performance, utilization rates, and production metrics. • Predictive Maintenance: By analyzing machine data, MachineMetrics can predict potential failures, allowing maintenance teams to address issues proactively. • Enhanced Decision-Making: With comprehensive data analytics, machine shops can make informed decisions regarding process improvements, resource allocation, and capital investments. MEC (Mayville Engineering Company, Inc.), a leading U.S.-based contract manufacturer, sought to improve machine uptime and efficiency. By partnering with MachineMetrics, they achieved: • 15% increase in uptime • 20% increase in efficiency • Return on investment within 90 days Morgan Olson, a leading walk-in van body manufacturer, transitioned from a paper-based tracking system to MachineMetrics’ automated data collection. This shift led to: • 20% boost in machine utilization within months • $600,000 savings in capital expenditures • 50% reduction in waste Video filmed at IMTS - International Manufacturing Technology Show Graham - Eric - Ben - Tim - Brady - Bill - John - Morgan - Henry #MachineMetrics #IMTS

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.