The six key challenges for utility digitization
Urbanization and globalization are driving new economic, environmental and social demands for efficiency, safety and optimization. OEMs, wholesalers and installers must determine how to navigate these demands.
Fortunately, at the heart of the paradigm shift from analog industrial processes to digital products and solutions are smart sensors. Across industries, smart sensors are the driving force behind implementing connectivity. They empower businesses to achieve ambitious optimization goals.
The Industrial Internet of Things (IIoT) has made a smooth transition from analog sensors to digital smart sensors possible. No longer limited to passing an electrical-measurement signal along, smart sensors collect, convert and process data — and offer machine-to-machine communication. This enables predictive maintenance, improved productivity and flexible and responsive manufacturing. Businesses then experience entire value-chain optimization, achieving more with less in the Industry 4.0 marketplace.
From mobile hydraulics to water management and from marine to wind power, smart sensor connectivity allows industries to use resources in smarter ways. That means businesses can maintain a competitive edge and respond to emerging trends such as smart cities and electrification. Below are five key points to address for utility digitization.
The Challenges & Payoffs of Digitization
With every great industrial innovation comes the challenge of implementation. In the case of Industry 4.0, technical integration, organizational transformation, data security and future viability all play a role in assessing how to integrate IIoT technologies into business models.
Smart sensors are the framework for taking full advantage of IIoT and major payoffs await on the other side of implementation. Data and information that is captured, processed, evaluated and communicated by smart sensors let organizations take action on optimizing industrial processes, vertically and horizontally.
No longer limited to process control, purposefully analyzing and processing manufacturing data through smart sensors fosters transparency of value creation. This opens the door to new opportunities, such as predictive maintenance, flexible manufacturing processes and improved productivity.
Ultimately, the main business challenge is not the adoption of Industry 4.0 technologies. It is the fact that industries must do more with less to remain competitive in the digitalized world. Smart sensor technology is one of the gateways to take advantage of IIoT and optimizing business models.
Transforming from Industry 3.0 to Industry 4.0
Industrial controls continue to evolve at a faster pace than ever before. Every single sensor on the plant floor, production floor or at the application site is going to be smart, connected and IIoT-ready.
To anticipate industrial controls of the future, one must understand the controls of today and why they are needed. Industry 3.0 marked the shift from manual push-buttons and relays to the first programmable logic controllers (PLCs) and digital human-machine interface (HMIs). PLCs made it possible to reconfigure an HMI screen when mechanical devices failed, saving cost and time.
Industrial controls could turn electrical devices on and off and control operational speed. However, data collection and analysis were not on the Industry 3.0 agenda. Its goal instead was to establish reliable process control. As such, data was collected manually with chart recorders and external hard drives. As PLCs, HMIs, software and embedded controllers became mainstream, automatically collecting data instead of using manual processes became a reality. This marked the dawn of Industry 4.0.
Industry 4.0, also known as IIoT, enables manufacturers to harness digitized, connected, smart, decentralized value chains through the integration of information technologies. It allows businesses to capture increased efficiency while preparing for the next wave of industrial innovation.
In the face of market volatility and complexity, smart sensor technology delivers the automation, safety and optimization through traditional PLCs and gateways. It is empowered by networks, machine-to-machine communication and machine learning.
Defining Smart Sensor Technology
Smart sensors are the backbone of Industry 4.0 and its success. Smart sensor technology makes industrial processes more efficient. They also have broader economic, environmental and social benefits. From trends such as smart cities to critical ecological impact such as reduced energy consumption, smart sensors are driving the world forward.
Smart sensors are responsible for enabling the automated and more accurate collection of environmental data, a linchpin for IIoT. A smart sensor is a device that takes input from the physical environment and uses built-in computer resources to perform predefined functions based on specific input. It then processes data before passing it on. If a sensor simply sends an electrical measurement signal forward for remote data processing without additional information, it is not considered a smart sensor.
At a minimum, a smart sensor comprises a sensor element, a microprocessor and a communication technology. Computer resources must be integrated with the physical design. A smart sensor also incorporates software-defined elements for data conversion, digital processing and communication to external devices. In more advanced cases, a smart sensor can support various sensing technologies, handling and delivering multiple measurement values. Used for monitoring and control mechanisms in a wide variety of applications, smart sensors are used in water management, mobile hydraulic, marine and offshore, wind power and HVAC applications.
Drive of Smart Sensor Technology
Smart sensors provide actionable data and information for predictive maintenance and troubleshooting. With an embedded micro-controller solution, smart sensors have the intelligence to take care of the signal conditioning and calibration of the sensor signal, while also handling and storing information inside the sensor. By storing data such as a part number, supplier information, target-application information, production date, and the software firmware version, one can improve application the complexity and quality of a monitoring system.
Some have automatic self-diagnostics that can reduce downtime by passing on diagnostic information for root-cause analysis. For example, maximum measurements recorded over the sensor’s lifetime, such as over-pressure and max. temperature, are valuable when investigating an application quality issue or field-return case.
By contrast, each analog sensor in an application control loop has to be individually wired to the controller interface often leading to complex and expensive cable-harness solutions that make troubleshooting difficult.
A smart sensor communicating directly on a digital fieldbus is daisy-chained to the fieldbus cable rather than running individually to the controller. This reduces the cable-harness cost and complexity and improves troubleshooting.
Optimized I/O Hardware Architecture
Customer environment and application digitization is driving demand for more sensors to control, monitor and improve system performance and efficiency. This poses a challenge to the physical limitations of controllers when multiple sensors need to be added to the hardware architecture. Controllers are often tailored — and therefore limited — to specific application needs. So the demand for more sensors bottlenecks hardware architecture input/output (I/O).
Changing analog sensors to digital fieldbus sensors can solve this I/O challenge. Most controllers have fieldbus capability for higher-level system communication, which simplifies the communication protocol down to the sensor and actuator level.
Unique Sensor Identification
Smart sensors provide interesting and relevant information to the controller through digital communication. Each sensor has a unique identification number, which can be related to specific application characteristics. Controller algorithms can use this information to evaluate whether it is connected to the correct sensor and hence is receiving valid data. Thus, it automatically detects if an incorrect sensor has been installed or two sensors have been mixed up.
Configuration Via Communication
A duplex or semi-duplex communication solution enables communication from the controller to the sensor that can be used to change, configure or fine-tune specific settings and parameters stored in the sensor.
This can improve measurement resolution and accuracy in the narrow measurement range of an application’s predefined operating mode. Moreover, this configuration reduces complexity. For example, a single sensor can be programmed to multiple measuring ranges depending on the installation point and application information that could be received by the controller. This reduces sensor part numbers in the customer’s supply chain to simplify day-to-day management.
The Power of Remote Technical Support
Digital communication capabilities allow application and product specialists to access sensor-setting files. A specific sensor configuration can be uploaded and downloaded through the communication protocol or an external sensor-communication tool. Technicians can remotely modify sensor settings to new application demands and requirements by evaluating a downloaded sensor setting file from the application field site.
This enables a remote simulation and evaluation of sensor behavior and diagnostics by technical experts, which can be beneficial when configuring or fine tuning sensors during design. A modified file can then be sent back to the application where it can be uploaded to the sensor, resulting in the new configuration of the sensor to take over immediately. Involving remote technical experts can likewise be helpful when evaluating diagnostics from the sensor in a critical root-cause analysis situation to minimize costly application downtime.
The Influence of Smart Sensors on Machine Learning & AI
A growing field of interest in a wide range of application control and monitoring systems is advanced analytics for machine learning and artificial intelligence (AI) algorithms.
Moving forward, advanced analytics will significantly increase machine intelligence – enabling the smart solutions of the future. Predictive maintenance, machine-health monitoring algorithms, and new ways of increasing system performance will be key. But the analytics perform only when fed with relevant data supplied by smart sensors.
Machine learning and AI algorithms can consume and react to smart sensor data such as the sensor’s serial number, model number, installation date, calibration history and self-diagnostic information – and application-specific data, such as who installed the sensor, when it was last powered up, and internal-measurement data.
Turning smart sensor data into actionable information is critical to the evolution of industrial businesses. Smart sensors make it possible to utilize connectivity and keep pace with innovation.
Embracing Smart Technology
Industrial innovation is a powerful force not only in business but in the evolution of our cities and the protection of natural habitats and precious resources. Smart sensor technology is the driving force behind the ability to optimize industrial processes, components and machines to meet the growing demands for meaningful solutions.
From preventing water loss to optimized district heating solutions, smart sensors play a pivotal role in providing innovative solutions to prepare for a healthier, safer and more productive tomorrow. Through greater accuracy and energy efficiency, primary industries can enable the sustainable growth of tomorrow’s megacities.