Machine Monitoring for Efficient Manufacturing
Machine monitoring is a supportive technology that has taken the automotive manufacturing process by storm. By leveraging the power of AI and Internet of Things (IoT) devices, manufacturers can closely monitor and analyse machine performance in real time. Machine monitoring allows to effectively operate automotive machines involved in the production process such as diecasting, stamping, welding, and blanking.
Third Eye AI has developed an efficient system that helps to allocate resources and identify the requirements to plan the need of labour force, equipment, and facilities for the different tasks of the machine, further it has a system to plan the production according to the demand required by the department to manage the operation control and managing production.
- Furthermore, the technology provides an intuitive, easy-to-read, and easy-to-use tool for monitoring operations. Additionally, the system enables to identify the situation where an erroneous result is reported and then identifies the cause of the error and rectifies it.
- In addition to this, the system also generates the MIS report required by the management to access the performance of the machine for faster decision-making, Moreover, the system sends ADONS alert notifications to operators and managers in real-time.
- Besides, the system allows for effectively manages overall energy consumption with planning and corporate-wide commitment.
- Simultaneously system allows maintaining of KPIs that measure asset level of productivity and the period during which types of equipment or machine is not functional or cannot work.
This procedure ensures maximum efficiency, minimizes downtime, and streamlines production processes.
Condition-based Monitoring
Condition-based monitoring (CBM) is a maintenance strategy used in various industries, including manufacturing, transportation, and energy, to monitor the health and condition of equipment or assets in real-time. The main objective of CBM is to identify potential issues and failures before they escalate, allowing for timely maintenance and reducing the risk of unexpected breakdowns.
Machine Monitoring Analytics
- Predictive Maintenance: Anticipating potential machine failures or performance issues before they occur, allowing for timely maintenance or repairs to avoid unplanned downtime.
- Condition Monitoring: Continuously monitoring the condition and health of machines to detect anomalies or deviations from normal operating conditions.
- Performance Optimization: Analysing machine data to identify opportunities for improving efficiency, reducing energy consumption, and optimizing processes.
- Data-Driven Decision Making: Using data insights to make informed decisions about maintenance schedules, equipment upgrades, and resource allocation.
Wrapping Up
Artificial Intelligence and machine monitoring are two intertwined pillars reshaping the automotive industry as we know it. From enabling autonomous driving and advanced driver assistance systems to optimizing manufacturing processes and ensuring predictive maintenance, AI and machine monitoring are driving innovation and efficiency at every level. This transformative duo not only enhances the driving experience but also empowers manufacturers with greater insights and control over their production processes.
