How IoT Monitoring Can Enable Predictive Maintenance

The internet of things (IoT) can be used for more than just remotely turning on your coffee pot; the IoT can also seemingly predict the future. Today, companies are turning to IoT-enabled remote monitoring tools that can use predictive maintenance to analyze how assets operate.

These new processes can increase productivity, lower maintenance costs, and eliminate downtime. Predictive maintenance, coupled with machine learning and artificial intelligence, can increase your return on investment by ten times.

Machine learning models are able to predict when equipment will fail and also prevent failure by adjusting parameters. If your devices do fail, predictive maintenance can detect these failures, ascertain their cause and then also present possible fixes. All of this is made possible by IoT monitoring. 

This article will examine how remote IoT monitoring works and how predictive maintenance makes it all possible.

What Is IoT Remote Monitoring?

IoT remote monitoring is the process of utilizing special sensors and equipment to interface with traditional assets like machinery and other technology. IoT remote monitoring is usually coupled with automation to assess whether any equipment is functioning properly. Before IoT remote monitoring, manufacturing plants and other facilities had to rely on staff manually inspecting machines to ensure their continuous functioning. IoT remote monitoring can help you stay productive, even if your teams are working from home.

For example, have you ever taken your car in for a routine oil change, only to be shocked to find that you need to replace something else too? IoT remote monitoring allows teams to avoid these shocks by collecting, processing and analyzing data before something fails. Remote monitoring technology makes diagnosing and maintaining processes easier because it can leverage all the data it collects. In the end, this means lower costs and better productivity.

Remote monitoring utilizes IoT technology and artificial intelligence to collect performance data on machines and equipment. Think back to the car example; imagine if your car came equipped to predict when something might go wrong. IoT devices do this by collecting data on equipment performance and then sending it across the web to services that utilize AI to analyze the data. These platforms comb through the information and can provide technicians with real-time access to performance metrics and insights.

Because data is transported off-site into the cloud, it can be persistent and stored for the life of the device and beyond. With this much data, personnel can build detailed reports and examine historical performance data. Over time, you can visualize machine productivity and, of course, predict maintenance. Remember, though, that since your data will be stored in the cloud, there are certain risks involved with the IoT. For example, hackers and bad actors can potentially abuse your data.

Using a combination of IoT insights, collected data, and machine learning enables teams to predict when breakdowns are most likely to happen. They can also recommend what actions you should take to prevent breakdowns and downturns from even occurring.

How Does Predictive Maintenance Work?

Predictive maintenance refers to a process that uses IoT remote monitoring tools that continuously analyze the performance and condition of an asset. Assets can be nearly anything, from machines to buildings, essentially anything connected to the web via IoT technology.

In a manufacturing context, predictive maintenance offers up to a ten times return on investment due to increased productivity. With such increases in productivity and profit, you’ll need to ensure that you have a secure bank account that comes with features like online account monitoring and no monthly maintenance fee. 

Predictive maintenance enhances throughput, minimizes maintenance costs and eliminates process breakdowns. For example, imagine you work in data storage services. Predictive maintenance can monitor air conditioning unit uptime and predict when load times might be highest. It can also predict when air conditioning units may need service. Rather than shutting down operations for maintenance, you can easily replace units before they fail.

By using machine learning, data is collected from IoT sensors that monitor your equipment, and this means you can prevent failures before they happen.

However, even if your assets or equipment fail anyway, predictive maintenance can also help predict what component has failed and the possible actions you can take to prevent a similar incident from occurring again. None of this would be possible, of course, without IoT and remote monitoring technologies.

How Does IoT Remote Monitoring Impact the World?

Remote monitoring is particularly valuable in manufacturing, where assembly lines rely on the continuous operation of all attendant machines. For example, imagine at the recently upgraded Tesla Gigafactory in Shanghai, a machine used for connecting doors to the vehicle stops working. When this happens, all downstream production is impaired, while upstream production becomes bottlenecked until the problem is fixed. The result is a crippled supply chain.

Without IoT remote monitoring, the machine simply breaks if maintenance fails to predict a future problem. Factory managers must then diagnose the problem, after which technicians are called in to try and fix things. If no parts are available, the machine may be unusable until they are ordered and arrive. Until the fix is complete, work is impaired. However, all of this can be avoided thanks to remote monitoring technology that can help prevent such downtime. 

IoT and predictive maintenance technologies aren’t just for manufacturing, though. Some companies use it in conjunction with blockchain technologies, supporting IoT deployment by improving security and decentralization. 

Blockchain, a decentralized ledger of all transactions across a peer-to-peer network, is most famous for being used with cryptocurrencies. The technology completely pseudonymizes crypto transactions for privacy, and assets can be held in secure crypto wallets that are thoroughly encrypted as well. This same technology can be used for IoT devices; its inherent decentralization means that third parties cannot hack a server to gain access to data, and it can also be used to track and authorize responses across the IoT network. 

However, Bosch, a well-known manufacturer of tools, began a test program using blockchain and IoT technologies to identify equipment problems last year. The hope is that Bosch will be able to leverage the technology to enhance machine learning that will, in turn, minimize future breakdowns.

What Strategies Are Used To Make Remote Monitoring a Success?

Finally, we’ll examine the three main strategies utilized to enable IoT remote monitoring.

1) Include Data Scientists in the Planning Stages

Data scientists are key at the early stages of any IoT remote monitoring implementation because they can make sure your project stays grounded in reality. For example, data scientists can instruct you on how to work with a data engineering team; understand the data you will be collecting, how to clean, structure and label it; and finally, how to create effective machine learning models.

2) Automate the Data Engineering Process

Data engineering is the process of collecting data from machine sensors and then moving it into storage, usually on a cloud platform. Data engineering needs to be reliable, reproducible and scalable. For example, if your facility has an unreliable internet connection, you may need to rethink how you will collect your data from devices.

3) Leverage Your Results

You won’t see huge returns on investment if you fail to leverage the data you collect. An essential part of your IoT project will be understanding what the data will be used for. If you plan on using it for maintenance, how will maintenance crews leverage data stored in the cloud? Do you need additional systems to process the data and make it useful? Who is responsible for overseeing all of this data? These types of issues all need to be overcome to reap the benefits of IoT predictive maintenance.

Nahla Davies

Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.

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