Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%

Intelligent Recognition of Acoustic and Vibration Threats for Security Breach Detection, Close Proximity Danger Identification, and Perimeter Protection

This article appeared in Homeland Security Affairs (March 2011), Supplement no.3The protection of perimeters in national, agricultural, airport, prison, and military sites, and residential areas against dangerous approaching human and vehicles when using human agents to provide security is expensive or unsafe. Because of this, acoustic/vibration signature identification of approaching human and vehicles threats has attracted increased attention. This paper addresses the development and deployment of three types of acoustic and vibration based smart sensors to identify and report sequential approaching threats prior to the intrusion. More specifically, we have developed: a) acoustic based long range sensor with which vehicles' engine sound and type can be identified, b) vibration based seismic analyzer which discriminates between human footsteps and other seismic events such as those caused by animals, and c) fence breaching vibration sensor which can detect intentional disturbances on the fence and discriminate between climb, kick, rattle, and lean. All of these sensors were designed with several issues in mind, namely, optimized low power usage, a low number of false positives, small size, secure radio communication, and military specifications. The developed vibration based system was installed in an airport with unprotected shore lines in the vicinity of taxi-and run-ways. The system reported an average of less than two false positives per week and zero false negative for the duration of forty-five days. Six fence sensors were installed on the terminal area and end-of runway chain-link fences where there was possibility of intentional fence climbing. The fence sensors reported no false positives for the duration of forty-five days which included several days of seasonal storms.Approved for public release; distribution is unlimited

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis