In industrial environments, asset availability is crucial, and the ability to continuously monitor their condition becomes a competitive advantage.
The Industrial Internet of Things (IIoT) offers this capability by connecting machines to collect and analyze data in real-time.
What is Industrial IoT?
The Industrial Internet of Things (IIoT) refers to the integration of sensors and connected devices within production systems, enabling real-time data collection and analysis. These devices measure parameters such as temperature, vibrations, pressure, and energy consumption of machines. The collected information is transmitted via secure networks to analysis platforms, facilitating continuous and precise monitoring of assets.
This interconnection transforms machines into active data sources, capable of signaling anomalies or maintenance needs before failures occur.
IIoT relies on technologies such as low-power wireless networks (like LoRa or NB-IoT), cloud platforms for data storage and analysis, and user interfaces for information visualization. By combining these elements, companies can shift from reactive maintenance to a proactive approach, thus optimizing the performance and availability of their facilities.
What can connected sensors concretely bring to maintenance?
Connected sensors, true digital sentinels, transform industrial assets into dynamic information sources. By continuously measuring critical parameters, they enable proactive monitoring and informed decision-making.
Sensors to listen to machines
Industrial IoT sensors come in several categories depending on the parameters they measure:
- Temperature: Essential for monitoring thermal variations in manufacturing processes, especially in the food, chemical, and metallurgical industries.
- Vibrations: Used to detect mechanical anomalies, such as imbalances or misalignments, by measuring asset vibrations.
- Pressure: Crucial for controlling hydraulic and pneumatic systems, ensuring the proper functioning of assets.
- Electrical current: Allow monitoring of the energy consumption of machines, thus detecting overloads or electrical anomalies.
- Mechanical wear: Measure component wear, anticipating replacement needs before a failure occurs.
These sensors, by capturing real-time data, offer increased visibility on the state of assets, thus facilitating predictive maintenance.
Data to anticipate and act
The information collected by the sensors is transmitted via secure networks to analysis platforms. This data allows for:
- Detecting anomalies: By identifying deviations from operational standards, systems can alert maintenance teams before a breakdown occurs.
- Optimizing interventions: Maintenance can be planned according to actual needs, thus reducing costs and downtime.
- Improving asset performance: Continuous monitoring allows for adjusting operating parameters for maximum efficiency.
By integrating this data into Computerized Maintenance Management Systems (CMMS), companies can shift from reactive maintenance to a proactive approach, thus optimizing the performance and availability of their facilities.
Connected sensors, by making assets “talkative,” provide maintenance managers with powerful tools to anticipate failures and optimize operations. However, implementing IoT projects involves technical, human, and financial challenges that need to be anticipated. In the next section, we will explore these obstacles and strategies to overcome them.
When AI gives meaning to maintenance data
Connected sensors generate a large amount of data. But to extract truly actionable alerts, artificial intelligence becomes a strategic asset.
From raw data to intelligent prediction
Thanks to machine learning, systems can learn to detect weak signals indicative of failure. For example, a subtle combination of temperature, vibration, and electrical consumption can indicate an anomaly well before a critical threshold is reached. AI identifies these patterns by relying on usage histories.
Towards prescriptive maintenance
Beyond prediction, some models recommend the best action to take, at the right time, considering available resources and the consequences of postponing an intervention. This is known as prescriptive maintenance.
CMMS and AI: a winning duo
Integrated with CMMS, AI improves planning, optimizes spare parts usage, and facilitates daily decision-making.
AI transforms IoT data into concrete, proactive, and increasingly targeted decisions.
What are the real challenges to anticipate in an IoT project?
Implementing connected sensors in an industrial environment is not just a matter of technology. Several obstacles can hinder or slow down an IoT project. Here are the main ones to know (and prepare for):
Technical integration: not always simple
- Old systems that are difficult to integrate with modern sensors
- Multiplicity of communication protocols
- Risk of data silos if the architecture is not well thought out
- Frequent need for gateways or adapters to enable machines to communicate with each other
Cybersecurity: an imperative
- Connected objects = potential targets for cyberattacks
- Data and access security is essential (encryption, firewalls, regular updates)
- Continuous system monitoring to be planned
- A cybersecurity strategy must accompany any IoT project from the start
Costs & ROI: choices to be weighed carefully
- Often high initial investments (sensors, network, software, training)
- Return on investment sometimes long to demonstrate
- Starting with a pilot project can help test without disrupting everything
- Set clear objectives to measure benefits
Human change: often underestimated
- Natural resistance to change (fear, skepticism, overload)
- New tools = new skills to develop
- Involve teams early on, explain the concrete benefits
- Training and support are essential for a successful transition
Before connecting your machines, you must think about compatibility, security, budget, human factors.
In the next section, we will see how to proceed concretely to deploy IoT without getting lost along the way.
Implementing IoT in industrial maintenance is an ambitious project that requires a comprehensive approach, combining technical expertise, financial strategy, and human support.
How to implement smart maintenance with IoT?
Transitioning to smart maintenance is not just about installing a few sensors on machines. It is a structuring project that requires method, coordination, and long-term vision. Here is a pragmatic roadmap to succeed in this transition.
- Identify critical assets
Not all assets require the same level of monitoring. Start by mapping your assets and prioritizing them according to their criticality: frequency of failures, impact on production, replacement cost, etc. This analysis will allow you to target the machines where IoT will bring the most value.
- Define clear and measurable objectives
Before deploying sensors, set specific goals: reduce unplanned downtime by 20%, extend asset lifespan by 15%, decrease maintenance costs by 10%, etc. These indicators will serve as a compass to evaluate the effectiveness of your project.
- Choose the right technologies
Select sensors and platforms compatible with your existing assets. Favor scalable solutions that can integrate with your maintenance management systems (CMMS) and data analysis. Also, ensure that communication protocols (LoRa, NB-IoT, etc.) are suitable for your infrastructure.
- Launch a pilot project
Before a large-scale deployment, test your solution on a limited scope. This pilot will allow you to validate technological choices, adjust monitoring parameters, and measure initial results. It is also an opportunity to involve field teams and gather their feedback.
- Train and support teams
The success of an IoT project relies on employee buy-in. Organize training sessions to familiarize technicians with the use of new tools and data interpretation. Set up support to answer their questions and assist them in this transition.
- Analyze data and adjust strategies
Once the sensors are in place, regularly collect and analyze data to detect trends, anticipate failures, and optimize maintenance plans. Use this information to refine your strategies and make informed decisions.
- Gradually extend deployment
After the success of the pilot project, develop a plan for gradual deployment across all your facilities. Prioritize high-stakes areas and adapt solutions based on the specificities of each site. Ensure constant communication with teams to ensure smooth adoption.
By following this structured approach, you will lay the foundations for smart and proactive maintenance, capable of transforming your industrial operations and optimizing the performance of your assets.
For further information: DimoMaint, your partner towards smart maintenance
Implementing a smart maintenance strategy relies as much on technology as on people and organization. Each step—from choosing assets to involving teams—must be thought out in a logic of simplicity, reliability, and tangible results.
It is with this in mind that DimoMaint has been supporting industrial companies for over 30 years. As a CMMS solutions provider, we offer an intuitive, interoperable, and field-oriented platform designed to integrate data from IoT and connected sensors. Our mission: to transform maintenance into a lever for sustainable performance by helping you anticipate failures, plan interventions at the right time, and extend the lifespan of your assets.
Glossary IoT & Predictive Maintenance
|
Term |
Definition |
|
IoT (Internet of Things) |
Network of connected objects capable of communicating data via the Internet. |
|
IIoT (Industrial IoT) |
Application of IoT in industrial environments. |
|
Sensor |
Device that measures a physical parameter (temperature, pressure, vibration…) and transmits it in digital form. |
|
CMMS |
Computerized Maintenance Management Software, centralizes work orders, history, parts… |
|
Predictive Maintenance |
Maintenance based on real-time data analysis to anticipate failures. |
|
Prescriptive Maintenance |
Advanced stage where AI recommends actions to take based on collected data. |
|
Machine Learning |
Branch of AI that allows a system to learn from historical data to make predictions. |
|
LoRa / NB-IoT |
Low-power wireless communication protocols, suitable for industrial sensors. |
