Improve factory security with iot

Improved maintenance equates to increased factory productivity. If plant managers fulfill their legal and ethical obligations to optimize site safety, productivity can be further improved. The combination of iot technology and big data analytics can enable factory security. Multiple KPIs can be monitored, such as employee absenteeism, vehicle accidents, property damage, near-misses, injuries, or any loss or damage that occurs during normal daily operations.

DS200LDCCH1ANA Often, many of these indicators can fall through the cracks if reported solely by humans, as they are either unreported or under-reported. Iot enables better overall security by ensuring real-time insights into these critical areas. Any issues that arise are addressed immediately, ensuring compliance with health and safety regulations and addressing environmental concerns.

Work-related injuries are a good example, as minor injuries are often not reported. Sometimes they develop into bigger problems, but the difficulty is how to connect the bigger problems to past events.

Iot wearables can provide a solution to this problem, as employees will be constantly monitored for various health indicators, including heart rate, exercise, activity, fatigue, stress, and more. They will also provide a way to communicate important safety information, reducing liability insurance costs and improving compliance across the workforce.

Digital tags can also help track employees. Tagging technology is specifically targeted at high-risk industries such as mining, allowing management to know exactly who is on the job site, how long they have been there, and ensuring that no one is left behind or left behind in an emergency.

Professional predictive maintenance sensors

Professional sensors also provide insight into plant conditions and any emerging problems. Iot technologies related to worker safety not only monitor workers, but also their immediate ecosystem. Outdoor workplaces such as construction sites and mines involve a variety of environmental factors that can endanger workers. Thermal imaging cameras and iot sensors can detect incoming adverse weather conditions and extreme temperatures. This information can be used to alert workers to these dangers. An activity tracker is an excellent example of a professional predictive maintenance sensor. They alert employees when they are near hazardous areas (for example, unstable or slippery surfaces).

DS200LDCCH1ANA Ai is contributing more and more to predictive maintenance

Another term related to condition-based maintenance is predictive quality and maintenance (PQM). PQM solutions leverage data collected from iot and legacy systems. They focus on detecting and addressing quality or maintenance issues beforehand before they develop into serious issues that lead to downtime.

PQM solutions leverage algorithms and generate average statistics to predict when quality correction or maintenance is needed. Ai-based PQM solutions integrate multiple technologies, including machine learning, deep learning, and cognitive computing.

Conclusion

To understand the true value of iot, it is necessary to take a holistic view of asset management. Iot technology, with its numerous on-site hubs that are connected to systems that collate data and perform complex analytics, provides new insights into plant conditions in real time. A high-performance virtual cloud network continuously collects, aggregates, and models data to predict failures. Emergency measures are in place to limit the impact on system availability. The key to iot improving drive costs and asset reliability is to deliver real-time, actionable and intelligent data to end users or connected systems. Factories welcome newer, more efficient maintenance possibilities to maintain a competitive advantage by constantly increasing uptime.