What Exactly is Predictive Maintenance?
Predictive maintenance is the practice of continuously monitoring the health of equipment within a company. By integrating IoT-connected sensors into machines, it is possible to collect information continuously and in real time. At the same time, companies use advanced analytics software based on machine learning (ML). These tools analyze data from IoT sensors to identify and resolve issues as soon as they appear. Some even go further by predicting potential future changes in equipment conditions.
Predictive maintenance is sometimes confused with preventive maintenance, which relies on a predefined maintenance schedule to detect faults and risks of failure (for example, servicing every X days). With IoT, these two types of maintenance can complement each other: depending on the actual condition of each piece of equipment, teams can adjust their intervention schedules and avoid unnecessary inspections.
What is the Role of IoT in Predictive Maintenance?
Predictive maintenance relies on various technologies, starting with IoT—connected sensors integrated into machines and equipment. These sensors collect data on performance and equipment condition, then transmit it to a platform accessible by managers. These platforms can incorporate machine learning algorithms to analyze the data. They can instantly alert teams to potential problems, respond autonomously, or trigger actions when certain thresholds are reached.
Common sensors used for predictive maintenance include:
- Vibration sensors: to detect mechanical irregularities and identify bearing or alignment defects.
- Acoustic sensors: to capture changes in sound frequency and early signs of mechanical wear.
- Thermal sensors: to monitor temperature variations and detect overheating equipment.
- Current sensors: to track electrical consumption and identify malfunctioning devices.
- Flow sensors: to monitor liquid and gas flow and detect leaks.
What Are the Benefits of IoT-Based Predictive Maintenance?
Adjusting a preventive maintenance strategy with IoT offers transformative advantages for industries, including:
Optimizing interventions
Not all maintenance tasks require the same level of expertise. By diagnosing problems remotely, companies can decide which type of technician is best suited for the job. For simple issues, a junior technician may suffice, while complex interventions require a more experienced professional. Additionally, having precise information about equipment condition allows technicians to work more efficiently and perform targeted repairs.
Reducing equipment downtime
By detecting issues early, companies can plan maintenance proactively, ensuring equipment continues to function smoothly. Machines are less prone to failure, increasing availability, productivity, and revenue.
Improving workplace safety
Employees can rely on machines that are functional and continuously compliant with standards. Predictive maintenance helps reduce the risk of accidents and prevent injuries on the job.
Extending equipment lifespan
Optimizing daily machine maintenance improves overall efficiency and lifespan, reducing replacement and upgrade costs.
IoT-Based Predictive Maintenance: Two Use Cases Explained
From factories and wind farms to power plants and smart buildings, many infrastructures benefit from IoT predictive maintenance. Here are some examples in France and beyond.
SNCF and Train Failure Prevention
Thanks to integrated sensors and high-speed 4G and 5G networks, SNCF can anticipate failures and ensure better train punctuality. A world leader in railway predictive maintenance, SNCF has equipped over 1,100 trains with SIM cards or IoT sensors. These devices can, for example, identify brake failure points and track deformations in rail tracks.
Predictive maintenance helps reduce service interruptions, improve traffic safety, and significantly decrease routine inspections. This is noteworthy, as systematic preventive maintenance accounts for 90% of SNCF’s maintenance activities. As a result, IoT-based predictive maintenance has halved the number of train breakdowns.
Better Water Management for Smart Cities
In France, millions of liters of drinking water are wasted each year. For cities, How is IoT transforming the water sector? intelligent and sustainable water management is a major challenge. To address this, connected sensors are attached to pipelines to collect data such as leak sounds, water flow, and pressure.
Using this data, cities are alerted in real time to potential leaks and can precisely locate them for quick action. With AI models, they can even detect early failures and intervene before leaks occur in the water distribution network.
Connected sensors, machine learning algorithms, and wireless communication technologies… technological advances are constantly expanding possibilities and giving IoT-based predictive maintenance immense potential. From manufacturing and energy to transportation, by revolutionizing equipment maintenance, it continues to gain popularity across industries. However, companies face challenges to deploy these technologies effectively, including integrating them into existing machinery, covering IoT deployment costs, and ensuring sensitive data security.
Also read: How to secure your IoT devices: 11 tips