Safety and maintenance are crucial for keeping infrastructure and equipment in functional condition. Maintenance ensures industrial productivity, while regular maintenance creates healthier and safer working conditions. Inadequate or absent maintenance can lead to serious health problems and fatal accidents. This article delves into how IoT sensors and software work together to provide solutions that promote production maintenance and safety.
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Maintenance capabilities have evolved from responding to system failures to planning, then to prediction, and finally (until now) self-healing. Advances in operational sensor technology, combined with successes in information technology, have facilitated the extraction of real-time performance data. These technologies include cloud-based analytics and platforms, augmented reality (AR), and virtual reality (VR), encompassing planned or predictive maintenance with negligible productivity losses.
Industrial maintenance can be categorized into the following types:
Corrective maintenance
Corrective maintenance is performed to identify, isolate, and correct faults so that faulty equipment, machinery, or assets can be restored to operating conditions within the tolerances or limits established for normal operation.
Preventive maintenance
Preventive maintenance refers to regular, routine maintenance performed to help keep equipment running smoothly and to prevent unplanned downtime and costly losses due to unexpected equipment failures.
Predictive maintenance
Predictive maintenance technology monitors the condition and performance of equipment during normal operation to predict when maintenance should be performed. It reports on the machine's status and operational capabilities by monitoring the values of specific variables, enabling data-driven decision-making.
Shutdown maintenance
By performing shutdown maintenance, components known to be prone to aging and usage-related degradation will be automatically replaced at a set frequency, with a replacement cycle shorter than the mean time between failures (MTBF). This measure can prevent unexpected failures and increase production output.
Regular maintenance
Regular maintenance is the maintenance performed on equipment according to a calendar schedule. It includes a series of major tasks such as data collection, visual inspection, cleaning, and lubrication.
Safety
Safety is paramount at all stages of production, including design, manufacturing, installation, adjustment, operation, maintenance, and final disposal. The Machinery Directive mandates that manufacturers guarantee that machinery and testing equipment (e.g., multimeters and thermal imagers) meet at least a minimum safety level. Machines and tools must comply with the "basic health and safety requirements" listed in the Directive, thus requiring a standard minimum level of protection.
How the Internet of Things can contribute to factory safety and maintenance
Implementing IoT solutions can fundamentally improve operational efficiency. IoT improves machine efficiency by tracking performance and can predict failures in advance. This eliminates unplanned downtime. Industrial IoT can also create safer workplaces. Industries investing in smart production and manufacturing systems want to achieve sustainable, optimal production with minimal maintenance. This makes maintenance a top priority. Systems such as Condition-Based Manufacturing (CBM) or Computerized Maintenance Management Systems (CMMS), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES) perform maintenance activities across multiple industries. These systems provide capabilities such as preventative and predictive maintenance, maintenance planning, scheduling, execution, traceability, and monitoring. eLink offers a wide range of maintenance, repair, and safety products from numerous leading brands. Use our extensive selection of components, tools, and equipment to ensure your production floor, machines, and safety equipment are well-maintained. (For more product details, please click here and here.)
Device status-based monitoring, predictive maintenance, and the Internet of Things
The machine condition within the CBM is continuously monitored by observing predefined equipment parameters. This reveals patterns that can be used to indicate equipment failure. The CBM system monitors parameters such as equipment vibration, temperature changes, oil level, motor voltage, and current. These measurements can be analyzed to generate appropriate action plans.
The Internet of Things (IoT) enables manufacturers and users to easily solve technical problems at a lower cost. Instrument sensors are now cheaper, more robust, more reliable, and offer a wider range of functions. Strong wireless protocols allow actionable data from sensors to be processed in a local gateway for immediate analysis and filtering. As shown in the diagram, this data can then be transmitted over the internet to cloud resources, which provide software-as-a-service to users of all sizes. This software can store the data and perform all the necessary analyses to identify trends and pinpoint potential points of failure.
Once these parameters are available for analysis, a failure model can be built to identify deviations from these benchmarks. Setting it up is quite straightforward if the combination of parameter values indicating a failure is known. A set of failure condition rules can be defined, and a correct model can be built using classical data analysis and mathematics. However, if the causes of the failure are not fully understood, it will be necessary to leverage data science and machine learning to develop algorithms capable of discovering significant patterns in the data.
Figure 1: Predictive maintenance via an IoT platform
Improving factory safety through the Internet of Things
Improved maintenance equates to increased factory productivity. Productivity can be further enhanced if factory managers fulfill their legal and ethical obligations to optimize on-site safety. The combination of IoT technology and big data analytics can ensure factory safety. Multiple KPIs can be monitored, such as employee absences, vehicle accidents, property damage, near misses, injuries, or any loss or damage occurring during normal daily operations.
Often, relying solely on manual reporting, many of these metrics may slip through the cracks because they are either not reported or underreported. The Internet of Things (IoT) enables better overall security by ensuring real-time insights into these critical areas. Any issues that arise can be addressed immediately, ensuring compliance with health and safety regulations and resolving environmental problems.
Work-related injuries are a good example because minor injuries are often not reported. Sometimes, they gradually develop into bigger problems, but the difficulty lies in linking the bigger problems to past events.
IoT wearable devices can offer a solution to this problem by continuously monitoring various health metrics of employees, including heart rate, exercise, activity, fatigue, and stress. They will also provide a way to communicate critical safety information, thereby reducing liability insurance costs and improving compliance across the workforce.
Digital tags can also help track employees. Tagging technology is specifically designed for high-risk industries such as mining, allowing management to know exactly who is on the job site, how long they have been there, and to ensure that no one is forgotten or left behind in an emergency.
Professional predictive maintenance sensors
Specialized sensors can also provide deep insights into factory 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 impending severe weather conditions and extreme temperatures. This information can be used to alert workers to these hazards. Motion trackers are an excellent example of specialized predictive maintenance sensors. They alert workers when they approach hazardous areas (e.g., unstable or slippery surfaces).
Artificial intelligence is making an increasingly significant contribution to predictive maintenance.
Another term related to condition-based maintenance is predictive quality and maintenance (PQM). PQM solutions utilize data collected from IoT and legacy systems. They focus on detecting and resolving quality or maintenance issues in advance, preventing them from developing into serious problems that lead to downtime.
PQM solutions utilize algorithms and generate average statistics to predict when quality corrections or maintenance are needed. AI-based PQM solutions integrate multiple technologies, including machine learning, deep learning, and cognitive computing.
Conclusion
To understand the true value of the Internet of Things (IoT), it's essential to approach it from a holistic asset management perspective. IoT technologies feature numerous field centers connected to systems that process data and perform complex analyses, providing new insights into real-time factory conditions. High-performance virtual cloud networks continuously collect, aggregate, and model data to predict failures. Contingency plans are in place to limit the impact on system availability. A key factor in IoT's ability to improve driving costs and asset reliability is the delivery of real-time, actionable, and intelligent data to end users or connected systems. Factories welcome the possibility of updated, more efficient maintenance, maintaining a competitive edge by continuously increasing uptime.
