Scheduled or predictive maintenance is a way to protect your business from service downtime. This, in turn, reduces costs associated with breakage.
However, predictive maintenance doesn’t come without upfront expenses. And if not fully aware of the current condition of your machine, you risk performing unnecessary or ineffective maintenance. The thing is - your car might work well after 10,000 miles of distance and break down within the next 100 miles.
So, how do you predict when to perform the next maintenance? The answer is IoT.
In this article, you will discover how to:
- adopt IoT and predictive maintenance in automotive;
- embrace IoT based predictive maintenance in healthcare;
- predictive maintenance using IoT in manufacturing;
- use IoT for predictive maintenance in telecom;
- mitigate top challenges of IoT-enabled predictive maintenance.
What are the most promising use cases of IoT and predictive maintenance?
Predictive maintenance is about constant monitoring of equipment conditions to predict issues. IoT sensors play an essential role in this process, as they gather data about different parameters (e.g., temperature, vibration, humidity, light, etc.) Then, this data is analyzed to predict when a failure may occur. With the help of IoT, maintenance is performed only when it is actually required, so it helps businesses avoid unnecessary maintenance expenses.
Now, let’s view how different industries, including automotive, healthcare, manufacturing, and telecom, can embrace predictive maintenance with IoT analytics.
IoT predictive maintenance in automotive
Efficient industrial IoT predictive maintenance in the automotive industry calls for reliable data for analysis and predictions. IoT sensors provide up-to-date data on the state of vehicle parts as well as send Diagnostic Trouble Codes (DTCs) that track existing mechanical failures.
With the help of IoT sensors, it is possible to monitor indicators, including fuel consumption, engine temperature, fluid levels, and run time. Here are some sensors types widely used in automotive:
- Oil & lubricant sensors
- Thermal imaging sensors
- Sensors that enable vibration, sonic/ultrasonic analysis.
Case study: IoT and predictive maintenance for vehicle state management
Our client is a UK-based car dealership company (under NDA). The company represents prestigious car manufacturers, including Jaguar, Audi, BMW, Mercedes-Benz, and more, and provides new and used cars and post-sales services for their clients.
This business has partnered with us to enhance equipment uptime and cut maintenance costs. They opted for an IoT predictive maintenance solution.
Collaborating with the development team on the client’s side, our experts work on the solution based on vibration data collected from pumps, motors, gearboxes, fans, and more. The data is collected either with the handheld devices within the rounds of inspection or online sensors permanently installed on the machine. The experts choose the approach depending on the available data, quantity, quality, type, etc.
IoT-enabled predictive maintenance for roadside assistance
Vehicle data help roadside service providers determine where the car broke down exactly and what’s wrong with it. It also helps indicate if roadside assistance is even needed: maybe the phone assistance about fixing the car would be enough.
IoT-based predictive maintenance for fleet management
IoT-enabled fleet management helps you understand where your vehicles are, their status, and who is in charge of driving and taking care of them. An IoT-based fleet management system can help your business with telematics (transmitting, storing, and receiving data from devices over a network) and alerts.
Fleet maintenance is needed to keep vehicles in good condition, so they are safe and serve longer. With the help of IoT sensors, it gets possible to perform vehicle diagnostics and fleet telematics extensions to work as a tool for predictive maintenance.
Predictive maintenance and IoT in healthcare
All the healthcare equipment should undergo regular inspection. Predictive maintenance, in turn, helps avoid service interruption due to unplanned device services. IoT sensors gather data on the state of devices like bladder scanners, blood pressure monitors, wheelchairs, and defibrillators. This data gets analyzed, and with the help of gained insights, experts identify what devices should be inspected or repaired.
Related: IoT in healthcare: key benefits and use cases
Case study: IoT-based predictive maintenance for medical devices management
WEINMANN Emergency is a German medical technology company that develops medical equipment ranging from oxygen systems, portable ventilators, and defibrillators.
Together with the client, the N-iX team has developed telemetry support for the smart defibrillator MEDUCORE Standard². With the help of this solution, it gets possible to prevent device failures (the ones related to defibrillation and monitoring). Based on a range of indicators, maintenance experts can identify issues with device sensors and their core components. Thus, real-time asset data enables equipment health monitoring.
Industrial IoT predictive maintenance in manufacturing
Real-time condition monitoring, powered by IoT devices, allows manufacturers to be more agile in their maintenance.
Case study: implementation of predictive maintenance and IoT
Fluke Corporation is a global distributor and manufacturer of electronic test tools and software. The company partnered with us for the development of a number of secure, reliable, and scalable solutions for enterprise asset management.
N-iX experts needed to implement reactive and preventive maintenance workflows. To do that, we have developed mobile solutions with top-notch offline support that interoperates with PLC/SCADA and CMMS systems. Apart from that, N-iX specialists have implemented the system supporting new industrial protocols that enable condition-based asset maintenance.
IoT analytics predictive maintenance for asset management
Modern plants call for more expensive equipment. And this equipment calls for more expensive maintenance. As a result, the cost of machine failure also increases. Each halt in production can lead to thousands of dollars losses. So, asset management gets critical.
With the help of IoT, manufacturers can get insights into the state of devices. For instance, if a particular machine heats more than it should while working, there is a risk of failure. IoT sensors that track temperature send an alert to the system, and experts check on this device.
Predictive analytics using IoT for supply chain management
Companies are ready to invest in predictive analytics in the supply chain to achieve increased visibility into operations as well as cut down expenses. Here is the list of the main pros of IoT predictive analytics in supply chain management:
- Tracking the state of equipment
- Preventing interruptions of supply chain
- Detecting inefficiencies
- Predicting and addressing risks
IoT predictive maintenance in telecom
IoT analytics predictive maintenance for identifying network errors
Telecoms are working hard to enhance network quality to satisfy the end-user. Fixing network errors precisely and quickly is the primary way to improve the quality of services. Ad hoc maintenance is often pricy, though. So, businesses opt for using IoT-enabled predictive maintenance and other technologies (e.g., big data) to predict network failures.
A telecom network consists of radio nodes, transport networks, switching centers, and civil infrastructure. With the help of predictive maintenance using IoT, telcos are able to maintain radio nodes in different locations.
The main benefit of predictive maintenance with IoT analytics is controlling the time and cost of the upkeep by pre-planning.
Despite the attractive benefits and promising use cases, IoT-based predictive analytics comes hand in hand with a number of challenges. What are they, and how to address them? Let’s find out together.
Top challenges of implementing non-industrial and industrial IoT predictive maintenance solutions
1. Data overload
The amount of data that IoT devices collect in each industry can be overwhelming. As a result of enormous data piles, drawing conclusions has become a real problem. With the increase in the number of devices, the process of analysis and decision-making becomes more difficult.
Solution: Partner with the vendor that has extensive data expertise.
2. IoT integration with the existing system
Securely integrating new systems with existing ones is a real challenge for businesses that want to adopt IoT predictive maintenance solutions. A poorly secured or ineffectively monitored IoT solution is vulnerable to threats. So, it is critical to take all the security measures possible. For instance, secure network protocols, such as message-passing protocol, point-to-point encryption, and security certificates, are essential for ensuring security.
Solution: Partner with the vendor that has a streamlined security policy.
3. Difficulty in implementing predictive maintenance using IoT
Often businesses find implementing IoT-based predictive maintenance harder than they had expected. As proof of concept projects had been set in motion, many of these companies had identified concerns related to a lack of technical expertise, data portability, and transition risk.
As a result, many companies pause the integration of IoT and predictive maintenance, as they learn that it might take longer than planned and that return on investment might be longer than expected.
Solution: Find a vendor that will perform a thorough discovery phase - the first stage of the project, when you get the understanding of what to start with, what the risks are, and how to align your business goals with specific user needs.
Why implement IoT for predictive maintenance with N-iX?
- N-iX has over 2,200 tech experts onboard that ready to support your project with relevant domain expertise;
- We are compliant with the security standards and regulations, including ISO 27001:2013, PCI DSS, ISO 9001:2015, GDPR, and HIPAA, to ensure the security of your data;
- With over 21 years in the market, we have gained our expertise in such domains as Cloud, Big Data, Business Intelligence, Data Science, Data Analytics, Artificial Intelligence & Machine Learning, and others.;
- We have established long-term partnerships and developed IoT solutions for companies such as Fluke, Bicyklen, and Fortune 500 companies.
