Smart Sensors with Self-Diagnostics and Repair Capabilities

Smart sensors can also be well-suited to safety-critical applications like detection of hazardous gas, fire, or intruders. Conditions in these environments can be harsh, and the sensors can be difficult to access for maintenance or battery replacement, yet high reliability is critical. A team at the Lab-STICC Research Center, University of South-Brittany, has been developing a solution that improves reliability by using dual probes and hardware that can self-diagnose and repair itself.

The ultimate goal of their project is to integrate all the elements described into a single discrete device, suitable for applications such as hazardous gas detection in areas such as harbors or warehouses. The project centers on a node that can pinpoint an internal failure and take corrective action to improve both reliability and energy efficiency. This reduces the node’s vulnerability and alleviates maintenance costs. The design recognizes the limitations of such sensors: restricted battery autonomy, energy harvesting subject to unreliable energy source behavior, limited processing and storage resources, and a need for wireless communications.

Figure 3. Hardware configuration of a wireless sensor node. (Image: ©Premier Farnell Ltd.)

The node is equipped with two sensors; during normal operation, the first captures environmental data while the second is only activated by users to verify the obtained data. If the first sensor were to fail, the node’s reliability is downgraded, while battery power is being wasted on supplying the non-functioning sensor. However, if the node disconnects the first sensor and switches to the second, no energy is wasted and node reliability is maintained.

Accordingly, the project’s objective was to develop a novel self-diagnostic based on functional and physical tests to detect a hardware failure in any component of the wireless sensor node. This method can identify exactly which node component has failed and indicate suitable remedial action.

Figure 3 shows the hardware configuration of the self-reconfigurable sensor node. Its components include a processor, a RAM/FLASH memory, an Interface for Actuator and Sensors (IAS) to interface with the environment, a Radio Transceiver Module (RTM) to transmit and receive data, and a battery with power switches (DC-DC converters). The node also includes a Power and Availability Manager (PAM) combined with an FPGA-configurable zone. The first one is considered as the intelligent part for the best use of energy, auto-diagnosis, and fault-tolerance, while the other enhances the availability of the sensor node.

Figure 4. Issues and corrective actions for a self-diagnostic sensor node. (Image: ©Premier Farnell Ltd.)

The table in Figure 4 shows how the sensor node can respond to various node issues. The FPGA contains a softcore 8051 CPU that is activated when performance enhancement is needed or to replace the main processor if it fails. The FPGA is an Actel type IGL00V2, chosen for its reliability and low power consumption. The remainder of the node comprises a PIC processor, RAM memory, Miwi radio transceiver module, two Oldham OLCT 80 gas detectors, LM3100 and MAX618 power switches, and a battery.

Conclusion

In this article, we have seen how chip manufacturers and researchers have been responding to the IoT’s need for smart sensors. This has partly been a matter of adding intelligence and communications capabilities to the basic transducer function, but it also involves improved fabrication. By integrating the MEMS sensor elements and CMOS computing components onto a single substrate, smart sensors can be implemented in small, low-cost packages that can be embedded in space-constrained applications with resilience to their environmental conditions.

Accordingly, IoT designers can source the sensors that they need — small, cheap, resilient, and low-power enough for ubiquitous deployment, while having the intelligence to deliver useful information as well as raw data. They also facilitate more flexible, granular automation, as they can accept incoming commands for recalibration to accommodate production changes.

This article was contributed by Newark element 14, Chicago, IL. For more information, Click Here.