Smart sensors are replacing conventional sensors in all industries to enable lower costs and reduced complexity for original equipment manufacturers (OEMs).
A smart sensor platform is used for predictive maintenance and instrumentation, to monitor and communicate the condition of key parameters more efficiently, such as equipment vibration.
Smart sensor system maturity in the automotive industry has been analyzed based on two aspects, the ease of use and the rate of breakthrough innovation. The ease of use is low because before implementing a smart sensor, predefined embedded functions are required during sensor design, which inhibits its scope of application. The rate of innovation is low as well, which means the technology is not fully developed and still needs improvement in terms of efficiency. Innovation in the allied industry such as aerospace is high and its best practices is expected to be employed in the automotive industry.
In the automotive domain, smart sensors can be employed for unique identifier for applications such as clutch, break, accelerator pedals, transmissions, gear shifters, steering axis, valves, clutch plate, suspension, tires, and others. In addition, for the above purposes, smart sensors are used for condition monitoring.
Data is acquired with the help of sensors, such as fiber optic sensors, acoustic emission sensors, accelerometers, strain gauges, ultrasonic sensors, piezo-resistive sensors, etc. The amount of data can become very large when the structures are instrumented with a variety of sensors. Well-designed data acquisition algorithm, microprocessor unit, data sampling and recording is required to successfully acquire large volumes of data.
Data needs to be conditioned, amplified, and processed before it is stored in a database because of the uncertainties in the field. Data obtained with the help of various sensors are likely to contain noise. The goal of intelligent processing is to remove this unwanted or redundant information. Data management strategies should be able to eliminate unnecessary data without sacrificing the integrity of the overall system.
Diagnostics is performed with the help of microcontroller and microprocessor units, which involves further interpretation of the processed data, analysis of the responses of the structure, and identifying if any foreseen damage or deterioration has occurred. Diagnostics is concerned with converting the data from monitoring to useful information about the response and health of the structure. The diagnosed data can then be stored for later purposes of prognosis. In some cases, data can be stored for long periods of time, and it is important that once retrieved, data is easy to understand. Prognosis is used to evaluate structural damage in the past and predict the capability of the structure for future use.
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The data from diagnostic and prognostic analysis is further used to estimate the remaining life of the structure. It is also used to perform the cost and benefit analysis and schedule maintenance for the future.
Data communication refers to the transfer of data from the data logger to the end user. The communication of data is an important aspect of an effective monitoring system; it eliminates the need for site visits and inspections by the engineers.
Smart sensors are replacing conventional sensors in all industries to enable lower costs and reduced complexity for original equipment manufacturers (OEMs). In addition, individual sensors can be added to the smart platform to transform it into a smart sensing device or an intelligent sensing device. Smart sensors will play an enabling role in the Internet of Things (IoT) revolution as more devices can be easily connected with each other.
Building lightweight vehicle with high strength, companies such as Tesla have shifted their focus from a metal airframe and structure to a composite and aluminum structure. With respect to the change in structure, a smart sensing platform will be needed that can be integrated into the composite structure of the vehicle to monitor its lifetime, from manufacturing to operation. In the future, the automotive industry will witness a new generation of smart sensing platforms that can be embedded in the structure itself.
Industries are emphasizing the need for enhancing sensor functionality and forming a network of sensors, which further leads to the need for autonomous computing and energy harvesting. Sensors can help businesses utilize their assets more efficiently and innovatively and increase end-user ROI, resulting in additional revenue streams. Continuous structure monitoring by integrated sensor networks can lead to improved damage discovery at a much earlier stage and enable continuous detection of evolving damage and, at the same time, heal the damage without disturbing the ongoing operations.
We have identified certain emerging technologies and the integration of smart sensors, which will create an impact in future.
Bio-inspired material is the product of a natural outcome of a biological activity such as the human nervous system. Expandable networks carrying multiple types of sensors will be deployed and integrated into the structures. Diverse multi-scale processing techniques are used to manufacture bio-inspired intelligent material. The material will be fabricated using complementary metal oxide semiconductor (CMOS) and MEMS processing on polymer substrates using piezoceramic material. At present, the material is used to detect resistive temperatures. Bio-inspired materials are gaining traction among OEMs such as The Boeing Company as well as research laboratories and academic institutions working towards developing intelligent materials.
When damage is inflicted on an autonomic healing compound, an immediate trigger response helps prevent further material loss, which is somewhat similar to blood clotting. This step is followed by material transportation, which is necessary for the actual healing to take place. Healing can be performed in a variety of ways, depending on the application, and across different time frames. Materials contribute to the real magic behind sensing and self-healing applications and help transform different markets and industries. Self-healing sensor enables high-performance devices, increases lifetime, and reduces the cost for condition maintenance in a slew of applications in sectors ranging from aerospace, oil and gas, automotive, energy, construction, and defense.
Continuous monitoring of the automotive structure by integrated sensor networks can lead to the discovery of damage at a much earlier stage and enable continuous detection of evolving damage. At the same time, the damage can be healed without disturbing the ongoing operations.
Because of excessive weight and stress concentration, bolted composite joints are not optimal. Joints adhesively bonded have an additional advantage such as providing great strength and reducing weight. A smart adhesive film with a built-in network of micro sensors monitors the bondline integrity.
A composite material embedded with the network of sensors can predict the damage propagation throughout the material’s life cycle. The intelligent material can monitor the material quality and its manufacturing process as well as sense the current damage and state of the structure.
A system-level integration of a multifunctional smart sensor network includes structural, electrical, and thermal subsystems as well as multidisciplinary design optimization algorithms to optimize the performance of the structural battery system, in terms of cost, efficiency, and robustness.
Other Associated Technologies for increasing adoption of smart sensors
Energy Harvesting: Energy harvesting technology has generated much interest in the research community because of its efficiency, reduced cost, and energy consumption. With research around increasing power density and thermoelectric energy harvesting, energy harvesting is quickly becoming relevant in terms of the future of automotive energy systems.
Wireless Network of Smart Sensors: Deployment of WSNs facilitate upgrading and streamlining the monitoring and controlling of various processes. Data acquisition from different field devices such as temperature, position, pressure level, and temperature sensors allows for better process management. The ability to optimize resources, both in terms of energy and workforce, is greatly increased as data can be accessed remotely. This technology is already employed in aircraft.
Drivers for Smart Sensors:
Self-Diagnostics: Proper functioning of sensors throughout the operation period is confirmed through self-diagnostic features. Incorporating self-diagnostics into the sensors is important because the current systems may not differentiate between signal changes because of sensor failures from changes caused by damage. In high speed operations such as racing events, reliability is crucial, otherwise the consequences can be fatal. Smart sensors come with the self-diagnosing capability and have a wide scope of adoption in vehicle monitoring applications.
Predictive Maintenance: To analyze external and internal structural damages, diagnosis subsystem provides periodic inspection data to the prognosis subsystem. The prognosis subsystem with damage evolution models helps to estimate the current integrity of the structure and at the same time derive the remaining life of the structure and the need to trigger the maintenance.
Advancement in Smart Materials: Multifunctional and smart composite materials are emerging as an attractive opportunity for developers to design the structure and enable micro-level monitoring. Smart composites hold immense potential in applications where weight reduction and size constraints are considered as these composites will not add any extra weight to the structure and will consume less power as well as facilitate long-term monitoring of operational performance.
Autonomous self-healing is an interesting opportunity that system developers can utilize. Smart materials can be incorporated with the sensing system, which should be capable of self-healing once the defects or damages have been detected. The self-healing materials will repair the damage to return the structure to the previous usable state.
Advancement in wireless smart sensors and integrating energy harvesting technologies: Adoption of the wireless smart sensor networking technology has the potential to improve the monitoring quality dramatically, with the help of WSN’s on-board computational, sensing, and wireless communication capabilities. Integrating energy harvesting capabilities will provide additional advantages such as eliminating the need for regular maintenance and helping devices operate autonomously.
Advancement in Sensing Technologies: Smart sensors, combined with embedded electronics, computing, informatics, and power management capabilities, can be treated as an auto-adaptive system. Structures with an auto-adaptive system would impact structural health monitoring.
Advancements in sensors will facilitate the design of further miniaturized, accurate sensors for automotive applications. Availability of miniaturized sensors (and sensor nodes for data communication and storage) facilitates installation in every possible place by embedding them in the structures. Low-fabrication costs, low-power demands, and high sensitivity make smart sensors a driving factor.
Reliability: Reliability is an important parameter to formulate appropriate architectures and economic models for automotive systems. Reliability describes the probability of a monitoring system failing to perform its function within a certain expected lifetime because of manufacturing variability, level of quality control, installation conditions or procedures, and robustness of error handling in firmware and software design.
Integration of systems: The smart sensor system is hybrid and can involve a variety of sensors because of inspection requirements of different structures. Wireless communication brings in additional complexities related to energy efficiency of communication protocols. Other challenges include synchronizing data and time, dealing with data losses, and needing large data management techniques. Sensor and communication mechanisms need to be suitable enough to be either bonded or integrated into the structure without causing unacceptable losses in the structural integrity.
ROI: Deployment of smart sensors will boost a company’s confidence in the validity of the information generated and the prediction models. With the deployment of smart sensors, inspection, maintenance, and insurance are the three cost factors that could be lowered in the short term. Economic benefits are driving the interest level of structure owners, structural engineers, and governments. Smart sensors will further reduce the cost in terms of labor, increase productivity, and reduce downtime by detecting the flaws as early as possible in the timeline of the structure.
Challenges restraining the growth of Smart Sensors:
Integration of the system: Smart sensor systems essentially consist of sensors, hardware, software, and communications, which are developed by different manufacturers and then integrated as a single solution. Interfacing among these different components from different vendors can pose a serious challenge in terms of interoperability when applied in the field as a single system.
End-to-End solution: No one-size-fits-all smart sensing solution is available in the market, and the solutions are tuned to the requirements of the actual end-user application and energy sources available. Few companies such as EnOcean GmbH and Lord Microstrain are offering a complete solution for a particular industry; however, for other applications, new products needs to be developed continually. Collaboration between all component participants in the value chain will solve this challenge in the long term.
Wireless smart sensor network challenges: Conventional WSN applications require a low data rate, small data size, and relatively low duty cycle. For autonomous vehicle monitoring, wireless smart sensor networks need to have high data rate, large data size, relatively high duty cycle, accurate sampling of many measurement points, synchronization, and communication of sensed data.
Energy harvesting and limitation in power density: Power density of micro energy harvesting is not high and can mostly power low-power consuming devices. Moreover, energy harvesting is generally used to enhance the battery life and does not act as the sole power supplier. This challenge is expected to have a lower impact in the longer term as technological advancements take place to tackle this issue.
Lack of standardization: Standardization is required to validate and certify the monitoring systems and technologies. The technologies being implemented successfully in the laboratory need to be certified to ensure reliable operations in actual conditions. Procedural initiatives are expected from technology manufacturers, operators, and regulators.
Heterogeneity of data: The presence of heterogeneous data challenges data analytics. The data becomes too large and complex to be stored, managed, and processed by traditional database management techniques.
Opportunities in the automotive sector will be addressed by Tier I and Tier II participants with the capability to explore new ventures by investing in basic architecture and infrastructure, even though the scope for entry today is minimal. Opportunities in this domain are better tapped by leveraging the existing competencies, building on basic platforms, and trying to impact and influence the market, which leads to a scenario with heterogeneous products available in the market. Self-healing is an emerging technology. In the near future, the technology is expected to converge with many different technologies such as Big Data to gain market traction. The key trend in this area is to manufacture easily deployable sensors with no wiring and with enhanced durability.
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