Maintaining Machine Health: Condition Monitoring in IIoT Age
Published on : Thursday 06-05-2021
By using a condition monitoring solution, one can visualise the findings of condition data and take appropriate actions when the situation deviates from the norm.
Manufacturing is a complex sector that requires human workers to oversee all aspects of manufacturing processes. Traditionally, checking and maintaining the health of machines or maintenance in manufacturing relies heavily on manual processes. However, with the growing dearth of workers, the industry is forced to embrace connected, smart solutions to meet the demands and boost productivity at the desired quality. The advent of the industrial internet of things (IIoT) and Industry 4.0 is already unleashing the huge value in industrial settings around the world. Today, manufacturers are increasingly leveraging IIoT-driven machine condition monitoring to resolve equipment issues and maintain machine health.
Condition monitoring typically enables product quality control by identifying equipment health, including engine temperature, cutting speed, spindle vibration frequency, and ambient parameters, such as temperature and humidity. In other words, condition monitoring is essentially monitoring parameters that define the health of a machine or asset.
Powering condition monitoring using IIoT
Using industrial IoT, manufacturers can derive condition data and assess it for predictive maintenance purposes. They can also take advantage of the data collected through condition monitoring solutions, offering significant benefits. These include product design improvements, new product development, yield production through outage prevention, minimise maintenance costs through effective incident management, and optimise inventory of spares by extending equipment life.
Due to unplanned downtime, organisations face a loss of millions of dollars. Machine failure cost a company around $260,000 per hour. As unplanned downtime is a severe problem in manufacturing, there are multiple reasons behind it. It includes the overuse of the machine, improper maintenance or lack of efficient tracking of machines’ condition. IIoT emerges as an effective solution for condition monitoring. Let’s have a look at the benefits IIoT provides for condition monitoring.
1. The internet of things uses cloud computing and stores voluminous amounts of data in the cloud. As multiple types of equipment are connected through IoT, a large volume of data is produced. Manufacturing companies can use industrial IoT to store an enormous amount of data and optimise storage capacity.
2. Remote monitoring is crucial to monitoring machines remotely. By using IIoT, manufacturing companies can monitor multiple machines from a particular location. They even do not need physical access to the machines. Industries like oil and gas and electric power leverage IoT remote monitoring solutions. IIoT makes it easier to monitor remote installations of equipment, like pipelines and drilling rigs. It does so by gleaning data about the health of the equipment.
3. As downtime costs automotive manufacturers an average loss of $79.32 per hour, IoT-driven condition monitoring facilitates reduced downtime and effective utilisation of maintenance resources. It supervises vehicle health based on the factors such as engine temperature, vehicle vibration, and fuel consumption.
4. IoT helps ensure effective maintenance of machines. IoT predictive maintenance tactics perform an analysis of equipment. By using this, operators can pre-schedule the next service of machines. The IoT predictive maintenance tactics observe machinery condition parameters to spot changes that are indicative of a developing issue.
5. When manufacturers perform condition monitoring for predictive maintenance, an IoT-based condition monitoring solution uses ML algorithms to arrive at the conclusions of equipment’s health while improving diagnostic accuracy. However, machine learning is a CPU intensive process that requires an adequate amount of computing power and parallel processing with several machines working in clusters. Using a cloud-based condition monitoring solution will provide the required computational resources to run ML algorithms efficiently.
Implementing successful condition monitoring program
Condition monitoring refers to the data collection function needed to define a machine’s reliability. Implementing successful condition monitoring is not just using new test instruments, but also making a move from preventive maintenance to predictive maintenance. It demands a management vision and commitment while requiring investments on multiple fronts.
If you are planning to implement an effective condition monitoring program, you should:
a. Identify critical assets that need to be monitored and potential risks related to their operation.
b. Accumulate condition monitoring data with the same test instruments measuring the same set of attributes.
c. Train technicians to construe and report condition monitoring data.
d. Make out the corrective actions to be taken when condition monitoring data show any anomalies.
By using a condition monitoring solution, you can visualise the findings of condition data and take appropriate actions when the situation deviates from the norm. Companies that provide a value-added service will have greater success as the recovery improves than those companies that just provide a product.
Bridgera is a Custom Software Development and Service Company that specialises in building custom smart/digital IIoT monitoring solutions for its clients. It drives new revenue and competitive edge by combining leading-edge technologies such as IoT, Analytics, Mobility and Cloud. Bridgera Monitoring is a customisable and configurable framework allowing its clients to create alerts and alarms by coalescing business rules based on condition monitoring.
Rockwell Automation offers condition monitoring products to help its clients keep their plant floor running productively by detecting potential equipment failures. Its XM® Series of intelligent I/O modules performs real-time processing of critical parameters used in analysing the current health and predicting the future health of industrial machinery. Emerson’s condition monitoring solutions are specific to the application and criticality of businesses’ machines, and deliver data companies can count on for accurate diagnosis of machinery conditions.
Condition monitoring across industries
In the pulp and paper industry, industrial IoT allows companies to monitor the condition of rollers in paper machines. Any uncertainty in one roller bearing can affect the quality of the produced paper and cause fluffing and changes in paper thickness. Implementing condition monitoring into roller bearings with vibration sensors can prevent a large percentage of quality issues.
In the automotive industry, the infiltration of moisture into the spaces and gaps in welded spots can lead to porosity, while temperature variations in welding machines can lead to a weld joint failure. Using industrial IoT to monitor the temperature and the level of humidity around a machine can significantly avoid incorrect placement and ensure the high quality of the welded products.
Conclusion
Manufacturers can implement the Industrial Internet of Things to reduce machine downtime. By implementing IIoT-based condition monitoring, they can perform better and gain high RoI. IIoT is evolving as one of the key technologies in the technology market. According to the report, the IIoT market is forecast to grow at a CAGR of 7.4 from $77.3 billion in 2020 to $110.6 billion by 2025.
References
1. https://bridgera.com/industrial-iot/
2. https://www.rockwellautomation.com/en-us/products/hardware/allen-bradley/condition-monitoring.html
3. https://www.emerson.com/en-in/automation/asset-management/asset-monitoring/condition-monitoring
4. https://www.marketsandmarkets.com/Market-Reports/industrial-internet-of-things-market-129733727.html