Internal Sensors Research Articles

Structural Impact of N-terminal Pyroglutamate in an Amyloid-β(3-42) Fibril Probed by Solid-State NMR Spectroscopy.

Extracellular amyloid-β (Aβ) plaques, primarily formed by Aβ(1-40) and Aβ(1-42) fibrils, are a hallmark of Alzheimer's disease. The Aβ peptide can undergo a high variety of different post-translational modifications including formation of a pyroglutamate (pGlu, pE) at N-terminal Glu3 or Glu11 of truncated Aβ(3-x) or Aβ(11-x), respectively. Here we studied structural similarities and differences between pEAβ(3-42) and LS-shaped Aβ(1-42) fibrils grown under identical conditions (pH 2) using solid-state NMR spectroscopy. We show that the central region of pEAβ(3-42) fibrils including the turn region around V24 is almost identical to Aβ(1-42) showing similar β-strands also at the N-terminus. The missing N-terminal residues D1-A2 along with pE3 formation in pEAβ(3-42) preclude a salt bridge between K28-D1' as in Aβ(1-42) fibrils. G37 and G38 act as highly sensitive internal sensors for the modified N-terminus, which remains rigid over ~five pH units.

Increasing Battery Safety of Lithium-Ion Batteries by Using Cell-Internal Glass Fiber Sensors for in-Situ Temperature Monitoring

Lithium-ion batteries (LIB), featuring high energy density and power density, are used in a wide range of applications. One of the most important requirements for their application is to ensure their safety and continuous performance under different operating conditions. In order to ensure the afore mentioned factors, in-situ monitoring of internal cell parameters, such as temperature, state-of-charge, and state-of-health of LIB is essential. The cell temperature is a critical factor for both the safety and the LIB’s health. Not only the average temperature, but also the temperature distribution within the cell must be considered. However, common battery management systems mostly rely on monitoring the surface temperature of the cells, while neglecting changes of the internal parameters. This is where the integration of optical glass fiber sensors into battery cells, which allow direct monitoring of internal temperature changes during operation, makes a significant contribution.In this work, different approaches for the integration of optical glass fiber sensors into battery cells have been evaluated. The focus was set on the complete integration of the glass fiber at specific positions within the LIB, while ensuring that both, the glass fiber, and the battery components remain intact. Furthermore, the influence of the integrated glass fibers on the performance of the LIB was investigated. Finally, their functionality as temperature sensors was evaluated.In a second step, temperature measurements were performed on LIB during operation, using both internal and external sensors. The results obtained clearly reveal an early detection of temperature changes by internal glass fibers compared to external sensors. This not only confirms the efficiency of the internal sensors, but also leads the path to improved battery safety by early detection of critical temperatures within the cell.

Augmentation approaches for geodetic permanent monitoring systems in dynamic urban environments

The consolidation of Building Information Modelling (BIM) on construction sites and the preparation of the Internet-of-Things (IoT) in urban city planning are on-going processes. Therefore, the operation of modern total stations in dynamic urban scenarios becomes inevitable and often reveals the limitations of state-of-the-art approaches. This paper discusses augmentation strategies for the automatic operation of total stations. Additional internal sensor information and external facility information can be used to increase the resilience of the systems and enable them to adapt to changing circumstances. This is demonstrated by the examples of a changing prism orientation due to vandalism, local refraction effects due to air-conditioning and dynamic obstructions due to traffic.

Review and Analysis of the Characteristics of IoT Sensors

Internet of things (IoT) is the concept of data transmission network between physical objects ("things"), equipped with built-in tools and technologies for interaction with each other or with the environment. The work is devoted to the inspection and analysis of sensors, how the IoT systems are associated with environmental objects. IOT uses a wide class of measurement tools, from elementary sensors (eg, temperature, pressure, light), consumption metering devices (such as intellectual meters) to complex integrated measuring systems. The parameters of 30 types of sensors for IoT are given, 2 of them of the type of temperature and humidity sensors, 2 type of pressure sensors, 13 sensors that measure the flow and amount of gas or liquid, 2 type of accelerometers, 10 approach and movement sensors and 1 noise sensor. Among them are 4 sensors for wireless sensory networks (WSM), a single-time medical sensor for internal blood pressure measurement, calorimetric, vortex and electromagnetic flow sensors and gas or fluid, accelerometer, and memes of memes, and e-memometers, and electromagnetic measurement motion, a device of measuring noise level in the range of sound vibrations. For some sensors, there are no metrological characteristics at all, at best there is only a range of measurements and resolution. The resolution ranges from 8 bits to 16 bits with a signal sampling rate from 0.5 Hz to 48 kHz. There is even a LDC1612 digital induction chip with 28 bits. WSM sensors are designed to work with DigiMesh, LoraWan and Arduino, Raspberry. Many sensors use outdated RS 232, RS 432, I2C. Keywords: internet of things, network, review, analysis, sensors, objects, metrology.

Controller Design for Electrohydraulic Actuator of Heavy-Duty Automatic Transmission Using Model Predictive Control Algorithm

High-quality shifting is an inelastic demand for the automotive industry, and it is worth exploring the design of controllers that can shift precisely according to the planned shifting strategy. This paper proposes a dynamic model of the automatic transmission up/downshift process, and the transfer function based on pressure is first given. The model was verified by comparing it with the experimentally obtained fluid pressure. Subsequently, a pressure control strategy suitable for heavy-duty automatic transmission shifting was explored by establishing a model predictive controller with pressure feedback control. Meanwhile, a Kalman filter-based observer was designed to solve the inconvenient installation of internal sensors. Finally, the proposed control model was optimised using a multi-objective genetic algorithm for the controller. The simulation results indicate that the proposed control strategy can be applied to heavy-duty vehicles for improving the shift quality.

Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.

Validity, Reliability, and Feasibility of Smartphone Application Intended for Home Measurement of Gait Speed and Other Gait Dynamics in Older Adults with Blood Cancers

Introduction: Gait speed is a cardinal measure of mobility and frailty recommended to be assessed in all older adults with cancer (Dale, JCO, 2023). Four-meter gait speed (4MGS) is strongly predictive of mortality and hospitalization in older adults with hematologic malignancies (Liu, Blood, 2019). Remote monitoring technology can expand measurement of gait speed beyond the clinic (Clancy, JCO CCI, 2023), and smartphone applications (App) have been developed and validated to measure meaningful gait metrics via embedded internal motion sensors (Manor, JMIR Mhealth Uhealth, 2018). However, the validity and reliability of a smartphone App for remotely measuring gait speed in older adults is not known, nor is the feasibility of using an App to measure longitudinal gait speed in older adults with blood cancers. Methods: We conducted two studies to (1) determine the validity and reliability of a smartphone App in measuring 4MGS in community-dwelling older adults, and (2) determine the feasibility of measuring 4MGS weekly for six weeks in older adults with multiple myeloma (MM) and myelodysplastic syndromes (MDS). For the validity and reliability study, we enrolled adults aged ≥ 65 years living in an independent living housing facility. Each participant self-administered two supervised and three unsupervised gait assessments at home via the smartphone carried in their pocket, with the App providing multimedia instructions on how to complete the test. For each gait assessment, the participants walked back and forth along a 4-meter strip of ribbon for 45 seconds. For the first two supervised sessions, while the participants were self-administering the gait assessment, the study team concurrently captured their walking performance with a video camera (as a gold standard). For the following three unsupervised sessions, the participants administered the gait assessment App alone. We obtained the time for the first instance of walking along the 4-meter ribbon before turning and compared the gait speed (i.e. dividing 4 meters by the time) between the smartphone and the video capture. For the feasibility study, we enrolled patients age ≥73 years with either MDS or MM cared for at Dana-Farber Cancer Institute. Patients self-administered the gait assessment described above alone at home once a day for six weeks after a dedicated teaching session with study staff. We reported completion rates and participants' feedback regarding acceptability. Results: 14 older adults (77.2±6.3, 13 females) completed the validity and reliability study. The smartphone 4MGS demonstrated strong criterion validity yielding a high correlation with the camera 4MGS ( Figure 1) along with high reliability as demonstrated by high internal consistency and low measurement error ( Figure 2). The smartphone 4MGS also showed good test-retest reliability over three unsupervised sessions (intra-class correlation coefficient 0.78). In the feasibility study, 12 patients (79.3±3.14, 4 females, 50% MDS, 42% MM, and 8% both) were enrolled out of 19 contacted for recruitment (63%). To date, nine have completed the study: 9/9 completed at least one 4MGS test, 8/9 (89%) completed at least three weeks, and 3/9 (33%) completed all six weeks. When asked, “How important is measuring your function and mobility to you?” on a scale of 1 (low) to 5 (high), 7/9 participants answered 4 or 5. When asked to rate their experience completing the walking tests on a scale of 1 (very poor) to 5 (very good), 6/9 participants answered 4 or 5. When asked the degree to which one agrees with the statement, “I feel that the walking tests are too burdensome,” on scale of 1 (strongly disagree) to 5 (strongly agree), 9/9 patients answered 1 or 2. Conclusions: The App was both valid and reliable in measuring 4MGS in older adults' homes. User acceptance was also high, and the app was rated as minimally burdensome by patients with blood cancers with the majority completing at least three weekly home 4MGS tests. Our findings support using existing smartphone technology to assess a key measure of mobility and frailty in the homes of older adults with blood cancers, allowing for more frequent longitudinal assessment of how blood cancers and their treatments impact older adult mobility and frailty.

Predicting VR cybersickness and its impact on visuomotor performance using head rotations and field (in)dependence

Introduction: This exploratory study aims to participate in the development of the VR framework by focusing on the issue of cybersickness. The main objective is to explore the possibilities of predicting cybersickness using i) field dependence-independence measures and ii) head rotations data through automatic analyses. The second objective is to assess the impact of cybersickness on visuomotor performance.Methods: 40 participants completed a 13.5-min VR immersion in a first-person shooter game. Head rotations were analyzed in both their spatial (coefficients of variations) and temporal dimensions (detrended fluctuations analyses). Exploratory correlations, linear regressions and clusters comparison (unsupervised machine learning) analyses were performed to explain cybersickness and visuomotor performance. Traditional VR human factors (sense of presence, state of flow, video game experience, age) were also integrated.Results: Results suggest that field dependence-independence measured before exposure to VR explain ¼ of the variance of cybersickness, while the Disorientation scale of the Simulator Sickness Questionnaire predicts 16.3% of the visuomotor performance. In addition, automatic analyses of head rotations during immersion revealed two different clusters of participants, one of them reporting more cybersickness than the other.Discussion: These results are discussed in terms of sensory integration and a diminution of head rotations as an avoidance behavior of negative symptoms. This study suggests that measuring field dependence-independence using the (Virtual) Rod and Frame Test before immersion and tracking head rotations using internal sensors during immersion might serve as powerful tools for VR actors.

Creating a Smart Safety Device for Women using IoT

Would it be moral if I said that even in today's 2020, women in our society cannot live independently? Every day there is a lot of news of physical violence, rape and violence against women, and this number is increasing, especially in big cities. The presence of CCTV helped to some extent but the suspect could not be identified at the time of the attack. The aim of this article is to enable women to use technology to become self-sufficient, thereby reducing crimes against women and girls in India. The application proposal reveals a practical, effective and portable product that can help women live safe, independent lives. The goal of the Internet of Things is to integrate everything we have created and implemented with SIM 800 GSM modules, force sensitive resistors, impact sensors, Bluetooth modules, LCDs, resistors, transistors, diodes, LEDs, Arduino UNO, buzzer, etc., PCB, breadboard, transformer, switch, Arduino compiler and Neo6mv2 GPS module. The proposed system has dual security where a sick woman can seek help and share her location through the emergency number. It can activate the system in three different modes depending on the situation required, and sometimes it seems to appear at strange or bad times. The victim can activate the device using the alarm button or Bluetooth module. The internal heart rate sensor, GPS module and GSM module will be automatically activated when the device is turned on. The device will constantly beep so neighbors know what's going on, police said. The device is equipped with a force-sensitive resistor to protect the device from any external force during any misbehavior or misuse. In the age of pepper spray and smart bracelets, such tools have become powerful and effective in separating women's lives from bad behavior

Wearable sensing electronic devices with health monitoring function

Wearable mobile terminal (Wearable Devices) is a wearable mobile terminal that integrates sensors, wireless communication and multimedia technology into the human body. With the development of computer technology, wearable devices can perceive, record, analyze, adjust, intervene and even treat various diseases so as to maintain human health. Wearable electronic products refer to the intelligent exchange of information with the outside world using their own internal sensors and chips according to their physiological function or adaptability to the outside world. Wearable devices have been edging research in recent years, this paper introduces the characteristics of smart wearable devices, the materials currently applied, the different modes (types) of sensors, and discusses how the sensor network used for monitoring and transmitting recorded data can process the data network efficiently and accurately. In addition, as the sensor needs to consume more power to record and process the information obtained from the body, the battery life of smart wearable devices shortens. This paper summarizes some major concepts and methods related to the energy-saving of sensor components. Battery life significantly impacts the future personalization and universalization of health monitoring devices.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Mapping Tree Water Deficit with UAV Thermal Imaging and Meteorological Data

The mapping of forest stands and individual trees affected by drought stress is a crucial step in targeted forest management, aimed at fostering resilient and diverse forests. Unoccupied aerial vehicle (UAV)-based thermal sensing is a promising method for obtaining high-resolution thermal data. However, the reliability of typical low-cost sensors adapted for UAVs is compromised due to various factors, such as internal sensor dynamics and environmental variables, including solar radiation intensity, relative humidity, object emissivity and wind. Additionally, accurately assessing drought stress in trees is a complex task that usually requires laborious and cost-intensive methods, particularly in field settings. In this study, we investigated the feasibility of using the thermal band of the Micasense Altum multispectral sensor, while also assessing the potential for modelling tree water deficit (TWD) through point dendrometers and UAV-derived canopy temperature. Our indoor tests indicated that using a limited number of pixels (< 3) could result in temperature errors exceeding 1 K. However, enlarging the spot-size substantially reduced the mean difference to 0.02 K, validated against leaf temperature sensors. Interestingly, drought-treated (unwatered) leaves exhibited a higher root mean squared error (RMSE) (RMSE = 0.66 K and 0.73 K) than watered leaves (RMSE = 0.55 K and 0.53 K), likely due to lower emissivity of the dry leaves. Comparing field acquisition methods, the mean standard deviation (SD) for tree crown temperature obtained from typical gridded flights was 0.25 K with a maximum SD of 0.59 K (n = 12). In contrast, a close-range hovering method produced a mean SD of 0.09 K and a maximum SD of 0.1 K (n = 8). Modelling the TWD from meteorological and point dendrometer data for the 2021 growth season (n = 2928) yielded an R2 = 0.667 using a generalised additive model (GAM) with vapor pressure deficit (VPD), wind speed, and solar radiation as input features. A point dendrometer lag of one hour was also implemented. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity, and air temperature, an RMSE of 4.92 (μm) and R2 of 0.87 were achieved using a GAM. Implementing leaf-to-air pressure deficit (LVPD) as an input feature resulted in an RMSE of 6.87 (μm) and an R2 of 0.71. This novel single-shot approach demonstrates a promising method to acquire thermal data for the purpose of mapping TWD of beech trees on an individual basis. Further testing and development are imperative, and additional data from drought periods, point dendrometers, and high-resolution meteorological sources are required.

A Soft Actor-Critic Approach for a Blind Walking Hexapod Robot with Obstacle Avoidance

This paper investigates a path planning approach for the walking and obstacle avoidance of a blind hexapod robot in various field conditions. Hexapod robots often perform field tasks in unstructured environments, and their external sensors are affected by weather and light. This paper proposes the use of internal sensors to sense the terrain and a slightly modified soft actor-critic algorithm to train the motion strategy. A hexapod robot is capable of walking smoothly on rough ground only using internal sensors that are not affected by weather factors, and the soft actor-critic approach is superior for overcoming high-dimensional issues for multi-degree-freedom robot motion in unstructured environments.The experiments showed that the hexapod robot not only traversed rugged terrain at a fixed speed but also possessed obstacle avoidance capabilities.

Acoustic Waves From a Distant Explosion Recorded on a Continuously Ascending Balloon in the Middle Stratosphere

AbstractA helium‐filled mylar balloon carrying a smartphone and infrasound sensors ascended to a stratospheric height of 35 km over the surface detonation of a chemical explosive, with a total acoustic propagation distance of 127 km. The smartphone was configured to collect multi‐modal data at high rates from internal sensors. Analysis of the data shows successful collection of the explosion signal by both the smartphone's microphone and its accelerometers, the first from an ascending balloon. Comparison of the acoustic signal with that collected by other infrasound sensors, both airborne and ground‐based, provides insight into the possibilities and limitations of collecting acoustic data from the stratosphere.

Development of a phase-change material-based soft actuator for soft robotic gripper functionality: In-depth analysis of material composition, ethanol microbubble distribution and lifting capabilities

Soft actuators have numerous applications in soft robotics due to their capability of mimicking quite complex motions observed in living things with deformable organs. In the present research, a programmable, low-cost soft actuator based on phase-change material (PCM) composites is developed, using silicon rubber as the matrix, ethanol as the phase-change fluid, and carbon fabric as the conductive path which generates the required heat for actuation. The required pressure for actuation is provided by the electrically-induced phase transition of the PCM (30W electrical power supply). FESEM images illustrate the homogenous distribution of ethanol microbubbles within the active area, with an average diameter of 14.66 μm, and their absence inside the passive layer. The temperature is monitored by an internal thermal sensor placed inside the composite, and controlled to prevent overheating (T > 80 °C) in the active area. The gripper fabricated by using this composite produces considerable motion using a low-voltage driving force (∼30 V). Moreover, it undergoes a sizeable displacement (∼9 cm) and bending angle (∼50°), considering the low amount of power input (30W). Additionally, the tip of the gripper reaches the maximum velocity of ∼1 mm s−1, and the designed sample is capable of lifting objects with varying shapes and weights (from ∼10g to ∼300g).

Temperature-compensated surface acoustic wave internal pressure sensor for nondestructive structural inspection of spent fuel canisters

Temperature-compensated surface acoustic wave internal pressure sensor for nondestructive structural inspection of spent fuel canisters

On coherently processing multiple arrays in underwater environments with spatial coherence losses from random medium effects and sensor position uncertainties

Under ideal circumstances coherently beamforming multiple distributed arrays should provide improvements over the standard approach of processing the arrays individually and combining them incoherently. These include improved directivity and higher gain. However, two major challenges when coherently processing spatially separated arrays in a real ocean are dealing with coherence losses or wave front distortions caused by random medium effects like internal waves and uncertainties in sensor locations. Some of the consequences are degraded beampatterns and poorer array directivity. In this paper, we perform a simulation-based study utilizing numerical propagation models along with theory examining the effects of wave front distortions induced by random medium effects like internal waves and sensor position uncertainties on coherently beamforming distributed arrays in representative ocean waveguides. Realistic realizations of wave front distortions are generated from actual internal wave measurements and models. The sensitivities of coherent processing to these effects are characterized by resultant beampatterns and losses in array directivity along with a quantification of the tolerances.

Human Activity Recognition with Smartwatch Data by using Mahalanobis Distance-Based Outlier Detection and Ensemble Learning Methods

Recognition of human activities is part of smart healthcare applications. In this context, the detection of human actions with high accuracy has been a field that has been working for many years. With the increase in the usage of smart devices, smartphones and smartwatches have become the constant equipment of these studies thanks to their internal sensors. Sometimes abnormal data are included in data sets due to the way the data were collected and for reasons arising from the sensors. For this reason, it becomes important to detect outlier data. In this study, step counter and heart rate sensors were used in addition to an accelerometer and gyroscope in order to detect human activities. Afterward, the outliers were detected and cleared with a Mahalanobis distance-based approach. With the aim of achieving a better classification performance, machine learning methods were used by strengthening them with ensemble learning methods. The obtained results showed that step counter, heart rate sensors, and ensemble learning methods positively affect the success of the classification. In addition, it was found that the Mahalanobis distance-based outlier detection method increased the classification accuracy significantly.

Adaptive technique for physical human–robot interaction handling using proprioceptive sensors

The work focuses on the development of an adaptive technique for the physical interaction handling between a human and a robot, as well as its experimental validation. The proposed technique is based on the deep residual neural network and dedicated finite state machine, where the states are the robot behavior modes and transitions are the switchings between the states that depend on the interaction parameters and characteristics. It ensures the human operator safety and improves the human–robot collaboration performance by implementing various scenarios. In the scope of this technique, the parameters of human–robot interaction are used to select an appropriate robot reaction strategy using data from internal robot sensors only, i.e. proprioceptive sensors. These parameters define the interaction force vector and its application point on the robot surface, which allow to classify the interaction within the set of predefined categories. This classification distinguishes interactions applied at the tool or intermediate link (Tool/Link), having soft or hard nature (Soft/Hard), as well as having different intention (Intl/Accd) or duration (Short/Long). Based on identified category and the current robot state, the algorithm chooses an appropriate robot reaction. To confirm the efficiency the developed technique, an experimental study was conducted, which involved the collaboration between the real industrial manipulator KUKA LBR iiwa and the human operator.

Study on self-diagnosis/prediction technology for LIDAR sensor of autonomous vehicles

Along with the development of autonomous driving technology, the need for self-failure diagnosis and Remaining Useful Life (RUL) prediction technology for core parts for autonomous driving is increasing. In particular, the characteristics of the light detection and ranging (LIDAR) sensor exposed to the outside further increase the need to apply fault diagnosis and RUL prediction technology considering various environmental variables. In this study, based on the accelerated degradation test of LIDAR, the failure mode was analyzed. Through this, LIDAR failure due to thermal runaway, which is the first failure type in high temperature conditions, was identified, and whether there were major environmental data that could identify thermal runaway was identified. In the case of LIDAR's thermal runaway phenomenon, a study on an algorithm to identify the precursor symptoms of failure in an accidental failure situation is conducted. Afterwards, through the actual vehicle test process, various environmental variable information is analyzed for correlation with LIDAR internal sensor data, and the abnormal data for the temperature of the internal parts of the LIDAR is predicted through the external environmental sensor.