Advances in IoT and sensor technologies make smart manufacturing both possible and affordable. Plant owners no longer need to run their facilities without total visibility into each and every process. No longer will they be surprised with unplanned downtimes and missed production schedules. They can now reap the enormous benefits offered by smart manufacturing technologies.
Smart Manufacturing demands real-time and historical sensor data to generate meaningful insights. Artificial intelligence (AI) and Machine learning (ML) applications demands compute near the source of data generation. The CIMCON iEdge 360 Platform is critical to unlock benefits from IoT, AI, & ML technologies to deliver better manufacturing outcomes.
Leveraging real-time data can provide a true “what if” scenario to various stakeholders. This actionable intelligence reduces operation costs, increases revenue, and increases efficiency and productivity.
The iEdge 360 Platform enables latency-sensitive smart manufacturing, offers greater business agility, lowers operating costs, and results in a faster return on investment from smart manufacturing installations.
Having access to unprecedented volumes of data and creating valuable insights from that data are a competitive advantage in the modern manufacturing business. Smart manufacturing technology with iEdge 360 provides real-time visibility into plant operations and the health of machines with the goal of improving manufacturing efficiency. The challenge is combining and correlating diverse data sources that vary in nature, origin, and life cycle. Plant operations intelligence is designed to collect sensor data generated on the plant floor, production-equipment logs, production plans, production statistics, and operator information. By integrating this information into the iEdge 360 platform, its analytics capabilities provide instant visibility into production facilities, production outcome predictions, and analytics which further support production improvements.
Plant operations intelligence solutions help plant management understand how to improve efficiency or how costs can be cut.
Our end-to-end solutions provide manufacturing facilities with actionable insights into their entire plant and machinery in 3 steps. The iEdge 360 device connects to a variety of machines and sensors using wired or wireless networks available on the plant floor.
iEdge 360 is designed with rapid configurability and commissioning in mind. A typical iEdge 360 device with 7 to 8 sensors takes less than 30 minutes to configure and validate.
iEdge 360 allows you to connect with all the leading cloud-based IoT management platforms out of the box, leveraging the infrastructure investments you already made.
Google Cloud IoT Core
The iEdge 360 is built with industrial use in mind. Right out of the box, it is compatible with all the common connectivity protocols used in industrial automation.
Once you have your plant assets connected, you can manage the entire plant from one central location using KPI’s and data visualization tools. This improves overall efficiency, reliability, and quality of the products being produced at the facility with minimum equipment downtime. In many instances, a company’s manufacturing facilities are distributed across multiple locations. iEdge 360 provides one dashboard to monitor and manage multiple geographically-distributed facilities.
The iEDGE 360 platform offers various Quality Analytics, which improve operational efficiency and increase product yield. It collects quality data and integrates it with other process data to provide real-time visibility into the quality of your products. iEdge 360’s Machine learning algorithms detect early quality issues as processes evolve. Then, it recommends control limits for optimized machine performance. The solution identifies hidden patterns, predicts future events, and helps correct parameters before quality deviates.
Predictive maintenance solutions use condition-monitoring sensor data and machine learning or rules-based algorithms to capture machine performance during normal operation. It can detect possible causes for concern before they result in failure. Predictive maintenance enables the reduction of both planned and unplanned maintenance activities by providing real-time, actionable intelligence. The iEdge 360 platform collects sensor data, then integrates and transforms it into actionable information. It flags high-priority areas requiring immediate attention. Predictive maintenance reduces maintenance costs, reduces asset lifecycle costs, and results in greater asset performance.
The implementation of predictive maintenance solutions is a journey that starts with collection of equipment parameters throughout the plant. Then, this data is analyzed by advanced machine learning algorithms to provide true predictive maintenance.
Leveraging the latest advances in artificial intelligence, machine learning, and multivariate statistical modelling, iEdge 360 can build a “Digital Twin” of your entire plant. This Digital Twin can be used to study the behaviour of your system over a wide range of operating conditions, optimizing performance. It can answer questions like, “What do I need to budget for maintenance this summer?”
Sensor data quality plays a vital role in industrial IoT applications as they are rendered useless if the data quality is bad. Artificial Neural network-based sensor data validation algorithms identify outliers, missing data, constant value, stuck at zero, and filter bad data from useful data. This helps to increase data science productivity and accuracy of anomaly detection and diagnostics models significantly.
Control room operators/subject matter experts who monitor number of assets/parameters have limited ability to visualize small changes in individual sensor behaviors and identify correlation with other sensor or machine faults. In SCADA/PLC, the threshold value set for alerts by the plant or OEM are for warning and protection and cannot detect small changes in sensor deviation. In reality, most equipments fail as a result of slow-progressing fault modes and faults progressing independent of time.
