The adoption of sensors in an Industrial IoT (IIoT) environment is a critical part of the process of digitalisation in industries like manufacturing, energy, logistics, and transportation. These sensors collect information that enable automation, predictive maintenance, in-time monitoring, and optimization of process etc. However, the deployment of sensor in an industrial environment is unique from technology, operation, and organisation perspective.
Legacy system compatibility is complex. There are several industrial systems continue to use legacy machinery that are using analogue signals than digital signals. Such equipment will need modifications by retrofitting sensors via custom mounts, special interfaces, or converters to provide compatibility between analogue I/O and computer-based systems. Specification-based interoperability, such as OPC UA or MQTT, are popular and eases integration.
Industrial environments, often have very harsh operating conditions which include vibrations, electrical noise, moisture, and extreme temperatures, and this will require ruggedised sensor designs. Sensors are protected by encapsulation, shielding, and materials that are of industrial grade to maintain performance and longevity.
Before integrating sensors, the network architecture needs thorough consideration and analysis. The sensors are often deployed on large factory floors, necessitating strong connectivity. Wired solutions over Ethernet or Modbus offer reliability of stationary solutions, whereas wireless standards such as Wi-Fi, Bluetooth, and Zigbee offer flexibility. Large-scale deployments requiring long range and low power are to use low-power wide-area networks (LPWANs) such as LoRa and NB-IoT.
Edge computing is quite significant in IIoT. Instead of transmitting all the data to the cloud, edge devices process sensor data locally to mitigate latency, increase reliability, and save on bandwidth requirements. This can be very helpful in time-sensitive applications such as quality control or robotic operations.
The other challenge is data management. IIoT systems capture huge amounts of real-time data that demand elastic storage, processing, and analytics systems. Industrial data lakes and cloud platforms perform long-term storage and integration with AI-based analytics instruments.
There is a big issue of cybersecurity. The attack surface increases as more sensors get connected to enterprise networks. Certain actions, like a secure boot, encryption, authentication protocols, and frequent firmware updates, aid in curbing risks. The division of networks and zero-trust systems is also a good practice.
Organisational readiness is very essential. Sensor integration affects production, competency needs, and decision-making. Employees should be educated to analyse sensor data, service equipment, and use analytics data. Planning involving IT and OT (Operational Technology) and management is essential to successful implementation.
Examples of the successful sensor applications in IIoT are predictive maintenance in production (e.g., vibration sensors to predict motor fault), energy monitoring in a factory (smart energy), and asset tracking in warehouses using RFID and GPS.
To sum up, the implementation of sensors in an IIoT environment is a challenging but rewarding process that facilitates data-driven decision making towards strategic flexibility. Industries can realise high levels of efficiency, cost reduction, and innovation by overcoming challenges in connectivity, compatibility, data processing, and organisational change with robust sensor integration.
