Digital transformation is nowadays at the very top of the strategic agendas of industrial organizations in sectors like manufacturing, oil & gas, energy and logistics.  A key element of the digital transformation of enterprises in these sectors is the digitization of their physical processes, which is empowered by the introduction of wireless sensors and cyber-physical systems in their plants. The latter are the cornerstone of the fourth industrial revolution (Industry 4.0), which entails the control of physical processes based on the acquisition and processing of digital information about their surrounding environment.

Prominent Industry 4.0 Applications for Wireless Environment Sensors

Industrial wireless sensor networks are among the key elements of any Industry 4.0 deployment, as they provide the means for acquiring information about the physical world, including physical quantities like temperature, humidity, vibration and more. Some of the most prominent examples of wireless sensor networks deployments in Industry4.0 use cases are:

  • Predictive Maintenance: Industrial enterprises are increasingly leveraging digital data in order to realize a shift from conventional condition monitoring and preventive maintenance, towards a predictive maintenance approach. The latter repairs equipment at the best point in time in order to minimize downtimes and maximize Overall Equipment Efficiency. To this end, predictive maintenance applications process data from a wide array of industrial sensors (e.g., temperature, vibration, thermal cameras, acoustic and ultrasonic sensors) as a means of providing credible estimates for the Remaining Useful Life (RUL) of industrial assets and scheduling their maintenance accordingly.
  • Quality Management: Industry 4.0 applications leverage data from wireless sensors in order to detect and/or predict defects and other quality problems of the production process. For instance, temperature data and quality inspection cameras are commonly used to provide quality control insights as part of quality management disciplines like Six Sigma and Total Quality Management (TQM).
  • Digital Simulations and Digital Twins: Industrial enterprises are nowadays simulating processes in the digital world as a means of running what-if analysis and identifying how to optimize their deployments. To this end, they develop digital twins of products, assets and production processes, which comprise a faithful digital representation of physical assets and processes in the digital world. In most cases, digital twins integrate wireless sensors networks as means of collecting information for the status of the physical objects in their environment such as their temperature or humidity. The latter are needed in order to accurately simulate the behavior of physical assets and processes based on knowledge about their actual condition.
  • Flexible Production Lines: In the Industry4.0 age, production lines are gradually become flexible in order to support highly customized production models like Made-to-Order (MTO) and Engineering-to-Order (ETO). The flexibility of such production lines is empowered by their ability to sense products and their surrounding environment using RFID readers, temperature sensors, laser sensors and more.
  • Environmental Monitoring: Various Industry 4.0 applications rely on environmental sensing. As a prominent example, moisture sensing, video surveillance of crop and weather sensors, can optimize harvesting and water use in agricultural scenarios. In this context, wireless sensors are key enablers of precision agriculture and of the emerging digitization of farming.

Industrial wireless sensors networks deliver significant benefits to all of the above applications. Specifically, they offer accuracy and robustness in information acquisition based on the use of smarter and smaller sensors, which comprise embedded CPUs and intelligent algorithms. Most important, they have much lower installation and deployment costs than conventional wired solutions, while at the same time offering more sensing points and leading to cleaner deployments.

Deployment Challenges and Solution Guidelines

Despite the proclaimed benefits of industrial wireless sensors, pragmatic integrated deployments in industrial environments are still in their infancy. This is largely due to the fact that effective wireless sensor deployments are associated with a variety of challenges including:

  • Power efficiency: The energy autonomy of wireless sensor networks is usually limited and hence its energy efficient operation is a prerequisite for supporting enterprise scale applications in industrial environments. Power efficiency is influenced by various factors such as the communication protocols used, the I/O needs of the application and the efficiency of the wireless network middleware.
  • Sensing Accuracy: The sensors’ accuracy is yet another factor that affects the overall efficiency of wireless sensor network deployments in Industry 4.0 applications. Industrial grade sensors must therefore provide accurate measurements, not only when operating in normal conditions, but also when deployed in harsh environments, including environments that comprise liquids and source of interference.
  • Scalable Storage and Data Management: Industry 4.0 applications are typically collecting, storing and processing large volumes of data, which must be managed in a scalable and cost-effective way. This is the reason why industrial wireless sensor networks are commonly integrated in the cloud in order to benefit from the capacity, scalability and quality of service of cloud computing.
  • Application Development and Analytics: The business value of Industry4.0 lies in the analytics, rather than on the acquisition of raw data. To this end, any deployment should come with tools and techniques for developing applications, including data analytics applications. Developers should be offered with well documented and easy to use APIs for processing sensors’ data and for integrating them into applications. Likewise, there is a need for integrating raw data within machine learning and other data analytics toolkits, as a means of transforming sensor readings to insights that can drive optimizations.
  • Visualization: Industrial wireless sensor applications should provide ergonomic visualizations of their data in order to facilitate the interpretation and use of data driven insights. Therefore, they shall be integrated with visualization frameworks that include dashboards and charts for BigData representation.
  • Real Time Processing of Streaming Data:  In several cases data have to be processed in short time scales i.e. in near real time. This is very common in use cases involving real-time alerts. This requires the integration of streaming analytics frameworks over data from heterogeneous sensors, which is generally a challenging task.
  • Security and Data Protection: Sensor datasets can in several cases be sensitive, as they are directly associated with trade secrets and other forms of Intellectual Property (IP). Protecting such datasets from malicious parties is a key to ensuring brand protection and compliance to privacy regulations. Likewise, industrial sensors deployments should offer strong security features, since this is key to avoiding attacks and their associated financial losses and downtimes.

Ubidot provides a range of solutions for industrial wireless sensors and their applications in Industry 4.0 settings. Our solutions address most of the above challenges and enable organizations to deploy and fully leverage sensors in industrial environments. They ease the deployment of power efficient, accurate and multi-purpose sensors, while at the same time providing a versatile environment for developing and deploying intelligent and secure applications. In a nutshell, Ubidot enables companies to start their Industry 4.0 safe and on the right foot.

Read more about our solutions at: https://www.ubibot.io/

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