How the IoT is Changing Predictive Maintenance

With newer technologies introduced year after year, there are many ways for industries to improve their operations. These advancements are a positive impact their bottom line. One of these advancements is the Internet of Things or IoT. IoT refers to the interconnection of devices used in everyday life to the web. Many companies are adopting this next generation of the internet to their business processes. They are especially choosing to use the internet when the process refers to their predictive maintenance programs and asset management.

We will look into the different and innovative ideas that companies are implementing the IoT for their maintenance programs designed to make every task more efficient.

Determining the Health of Railroad Tracks

KONUX, a German company, is using the IoT to ensure the condition of the track. The startup deploys sensors with algorithms on the track. These sensors are responsible for monitoring the amount and level of vibrations sent for each passing train. This system aids in identifying potential issues on the track. Addressing issues at their early stages before becoming worse gives engineers the advantage. It is quite a brilliant concept. According to the team implementing this method, it cuts the cost of rail maintenance by as much as 25%. Considering the massive expenses of maintaining rails, their ideas on incorporating IoT is a serious game changer.

SNCF, a railway company in France, also makes use of innovative techniques in enhancing the reliability, accuracy, and performance of their system. The Big Data Fab serves as SNCF’s data expertise center. This center is where the company generates new products and services for their business units and engineers. Big data solutions also help in refining levels of diagnostics and control, along with the implementation of predictive technologies that include yield prediction and predictive maintenance.

The company’s use of advanced data technologies enables it to produce a new value. They combine data and increases access to such information so their engineers can analyze the data as soon as possible. The data gathered also offers insights to engineers. They evaluate weak signals while analyzing potential causes of failures. Through real-time monitoring, they anticipate potential incidents or malfunctions and receive alerts before failure arises.

SNCF also handles various projects designed to implement machine learning. This learning aids in gaining insights from massive volumes of data captured by the rail network’s function and condition. Overall, their efforts resulted in a proactive system in detecting early signs of failure. This system allows them to address and rectify issues before they become detrimental to their service delivery processes.

Safeguarding Power Lines

At present, US electric firms spend about $6 billion to $8 billion each year to maintain the condition of vegetation that surrounds power lines through ground crews and helicopters. In addition to such high cost of maintenance, the current inspection method implemented remained limited in terms of the expected results obtained. For instance, electric companies experience the difficulty in accessing remote areas of power lines, as well as inspecting long stretches of lines in a shorter amount of time.

Thus, through the use of Beyond-Visual-Line-of-Sight (BVLOS) drones, it is possible to conduct a more efficient, much safer, and more efficient inspection of vegetation management. In fact, inspections performed using drones reduce the cost by up to 4 times the regular expenses when using flights in the line of sight.

Drones are also capable of capturing a more comprehensive and wide scope of data. This data assists electric firms in making an informed decision when it comes to their predictive maintenance tasks in the facility. Sharper Shape, the Finnish company that has introduced the use of drones in mapping utility networks, claims to capture in-depth information on millions of trees for a better analysis of power lines that are at high risk of damage due to vegetation.

Tero Heinonen, Sharper Shape’s CEO, stated that the deployment of drones for vegetation management cuts down costs by up to 30%. Aside from the cost-effective value of this form of technology, it performs tasks at a much faster rate while providing daily assessments. Unmanned aerial vehicles, combined with an automated analysis of data gathered, allows companies to maintain the condition of power lines minus the high cost.

Increasing Popularity of the Internet of Things

Gartner Inc., a US research and advisory company that offers IT-related insights to businesses, predicts that there will be almost 26 billion devices on the IoT by the year 2020. The company believes that this massive growth is due to the greater access to very reliable mobile networks without requiring a fixed line connection. Thus, it is expected that more businesses use IoT in increasing their efficiency and productivity. As a result, it can improve power management and cut energy consumption at plants since this technology can help adjust the environmental control systems by remote and automatically.

Accenture is optimistic about the popularity of IoT in various facilities and companies. In its report, the projected investment in IoT would reach up to $500 billion in 2020. This investment includes the utilization of powerful and robust data processing, machine learning, and inexpensive sensors at plants. These all contribute to the efficiency in performing diverse industrial processes while reducing costs.

IoT and Predictive Maintenance

For the most part, many companies rely on the IoT for their predictive maintenance techniques in managing their assets. Instead of conducting the frequent calendar-based inspections combined with component repairs and replacement, predictive techniques help inspect equipment for potential issues. Recent technologies also send alerts or notifications when there is a need for a part replacement. Sensors placed in the equipment are responsible for determining abnormal conditions. These sensors trigger work orders once there is a breach in the safe and reasonable operating limits.

By having a more effective predictive maintenance strategy set in place, conducting maintenance tasks will only be done when necessary. This approach helps cut down labor and parts costs that come with component replacement and repairs. Predictive maintenance is bound to expand as various systems are carried out with internet connectivity, thus making the IoT an essential part of every business.