Device Software Research Articles

Accelerating neurotechnology development using an Agile methodology.

Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.

Beyond the 510(k): The regulation of novel moderate-risk medical devices, intellectual property considerations, and innovation incentives in the FDA’s De Novo pathway

Moderate-risk medical devices constitute 99% of those that have been regulated by the U.S. Food and Drug Administration (FDA) since it gained authority to regulate medical technology nearly five decades ago. This article presents an analysis of the interaction between the 510(k) process —the historically dominant path to market for most medical devices— and the De Novo pathway, a more recent alternative that targets more novel devices, including those involving new technologies, diagnostics, hardware, and software. The De Novo pathway holds significant potential for innovators seeking to define new categories of medical devices, as it represents a less burdensome approach than would have otherwise been needed historically. Moreover, it supports the FDA in its effort to modernize the long-established 510(k) pathway by promoting the availability of up-to-date device “predicates” upon which subsequent device applications can be based, reflecting positive spillovers that are likely to encourage manufacturers to adopt current state-of-the-art technologies and modern standards of safety and effectiveness. We analyze the of characteristics all the De Novo classification requests to date, including the submission type, trends, FDA review times, and device types. After characterizing how the De Novo process has been used over time, we discuss its unique challenges and opportunities with respect to medical device software and AI-enabled devices, including considerations for intellectual property, innovation, and competition economics.

Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs: a controlled, optical coherence tomography angiography study.

To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography (OCT-A). Thirty patients with crowded discs, and 47 control subjects were enrolled in the study. One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm2 rectangular area. Radial peripapillary capillary (RPC) density, peripapillary retinal nerve fiber layer (pRNFL) thickness, cup volume, rim area, disc area, cup-to-disc (c/d) area ratio, and vertical c/d ratio were obtained automatically using device software. Automated parapapillary choroidal microvasculature (PPCMv) density was calculated using MATLAB software. When the vertical c/d ratio of the optic disc was absent or small cup, it was considered as a crowded disc. The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes (P=0.740). There was no difference in pRNFL between the two groups (P=0.102). There were no differences in RPC density in whole image (P=0.826) and peripapillary region (P=0.923), but inside disc RPC density was higher in crowded optic discs (P=0.003). The PPCMv density in the inner-hemisuperior region was also lower in crowded discs (P=0.026). The pRNFL thickness was positively correlated with peripapillary RPC density (r=0.498, P<0.001). The inside disc RPC density was negatively correlated with c/d area ratio (r=-0.341, P=0.002). The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.

Effect of Fiber Orientation on the Fatigue Behavior of Steel Fiber-Reinforced Concrete Specimens by Performing Wedge Splitting Tests and Computed Tomography Scanning

This paper shows the relationship, in steel fiber-reinforced concrete, between fiber orientation and fatigue response through the combined use of computed tomography (CT), digital image processing (DIP) software and wedge splitting test (WST). The WST cubes were extracted from conventional 150 × 150× 600 concrete prisms and a groove and notch were carved on different faces in such a way that in half of the test specimens the fibers are oriented mostly perpendicular to the breaking surface and, in the other half, the fibers are mostly oriented parallel to the breaking surface. Fiber orientation was obtained using a CT device and DIP software from a miniprism extracted from the previously mentioned concrete prisms. The results show that there is a strong correlation between the crack-sewing fiber orientation on the one hand and fatigue life and crack opening rate per cycle on the other hand. Cubes with a higher percentage of fibers perpendicular to the crack surface (i.e., with a higher efficiency index) show a longer fatigue life and a lower crack opening rate per cycle, while cubes with a lower efficiency index show a shorter fatigue life and a higher crack opening rate per cycle.

Model-based, fully simulated, system-level power consumption estimation of IoT devices

Internet of things (IoT) gaining more importance due to its crucial role in pervasive computing and also Industry 4.0. Since the number of IoT devices is scaling up to multiple dozens of billions, the importance of energy efficiency is significantly increased. With the consideration of huge variety of IoT device hardware and software, a comprehensive model and estimation methodology on energy consumption is necessary as an enabler. IoT devices are also frequently updated, upgraded and maintained because of the evolving nature of the requirements and market demands. Each and every such operation has an effect on the power consumption and arose the necessity for a new energy consumption modeling and estimation. This process is applicable for development of IoT devices, as well as the maintenance phase. Since the variety of designs is unlimited, and battery capacity is usually fixed, or a cost factor, a generic, fully simulated, model-based energy consumption estimation of IoT devices is crucial. In this study, we aim to address this problem via proposing fully simulated, model-based, system-level power estimation approaches, as well as their success rate in typical real-life scenarios. It can be seen that the proposed methodology has high accuracy over %97. For the realization of the best-proposed approach, we used Open Virtual Platform (OVP) as an instruction set accurate simulator.

Enhancing Malware Detection through Machine Learning: A Comparative Analysis of Random Forest and Naive Bayes Classification Systems

Malware, a type of malicious software encompassing viruses, worms, Trojans, backdoors, and spyware, poses a grave threat to the confidentiality, integrity, and functionality of computer systems, given their integral role in everyday life. To combat the escalating sophistication of malware attacks, deep-learning-based Malware Detection Systems (MDSs) have emerged as indispensable components of both economic and national security. Utilizing a dataset sourced from a repository, our research focuses on classifying observations into benign and malicious software for Android devices, employing machine learning algorithms such as Random Forest and Naïve Bayes. The dataset comprises 100,000 observations with 35 features, and our evaluation metrics encompass accuracy, precision, recall, and F1-score. This study underscores the significance of MDSs in safeguarding against evolving cyber threats, utilizing cutting-edge machine learning techniques to bolster defense mechanisms.

Development of algorithms for assessing the regional distribution of respiratory activity parameters based on the analysis of EIT results

The paper is devoted to development of a method and algorithm for assessing regional ventilation and perfusion based on the analysis of the tomographic cross section obtained by the EIT method. The method consists of chest cavity change in the conductivity field reconstruction results spectral analysis. Based on the spectral analysis of the results of monitoring the ventilation and perfusion component of the field of change in the conductance of the chest cavity, it allows to evaluate the distribution of the respiratory volume and perfusion. An algorithm for assessing the thoracic cavity VPR based on the results of EIT has been developed. The algorithm consists in monitoring the ventilation and perfusion component of the chest cavity conductance change field, followed by spectral or regression analysis of the monitoring results to assess regional ventilation and perfusion, followed by the evaluation of the VPR based on the results monitoring of the ventilation and perfusion component of the field of changes in the conductivity of the chest cavity, to assess regional ventilation and perfusion, followed by an assessment of the VPR. The results of the work were used in the development of software for the EIT device.

Simulation-based research for digital health pathologies: A multi-site mixed-methods study.

The advance of digital health technologies has created new forms of potential pathology which are not captured in current clinical guidelines. Through simulation-based research, we have identified the challenges to clinical care that emerge when patients suffer from illnesses stemming from failures in digital health technologies. Clinical simulation sessions were designed based on patient case reports relating to (a) medical device hardware errors, (b) medical device software errors, (c) complications of consumer technology and (d) technology-facilitated abuse. Clinicians were recruited to participate in simulations at three UK hospitals; audiovisual suites were used to facilitate group observation of simulation experience and focused debrief discussions. Invigilators scored clinicians on performance, clinicians provided individual qualitative and quantitative feedback, and extensive notes were taken throughout. Paired t-tests of pre and post-simulation feedback demonstrated significant improvements in clinician's diagnostic awareness, technical knowledge and confidence in clinical management following simulation exposure (p < 0.01). Barriers to care included: (a) low suspicion of digital agents, (b) attribution to psychopathology, (c) lack of education in technical mechanisms and (d) little utility of available tests. Suggested interventions for improving future practice included: (a) education initiatives, (b) technical support platforms, (c) digitally oriented assessments in hospital workflows, (d) cross-disciplinary staff and (e) protocols for digital cases. We provide an effective framework for simulation training focused on digital health pathologies and uncover barriers that impede effective care for patients dependent on technology. Our recommendations are relevant to educators, practising clinicians and professionals working in regulation, policy and industry.

Establishing Fully-Automated Fundus-Controlled Dark Adaptometry: A Validation and Retest-Reliability Study.

The purpose of this study was to establish and validate a novel fundus-controlled dark-adaptometry method. We developed a custom dark-adaptometry software for the S-MAIA device using the open-perimetry-interface. In the validation-substudy, participants underwent dark-adaptometry testing with a comparator device (MonCvONE, 59% rhodopsin bleach, cyan and red stimuli centered at 2 degrees, 4 degrees, and 6 degrees eccentricity). Following a brief break (approximately 5 minutes), the participants were bleached again and underwent dark-adaptometry testing with the S-MAIA device (same loci). In the retest reliability-substudy, participants were tested twice with the S-MAIA device (same loci as above). Nonlinear curve fitting was applied to extract dark-adaptation curve parameters. Validity and repeatability were summarized in terms of the mean bias and 95% limits of agreement (LoAs). In the validation-substudy (N = 20 participants, median age interquartile range [IQR] 31.5 years [IQR = 25.8, 62.0]), measures of rod-mediated dark-adaptation showed little to no between method differences for the cone-rod-break-time (bias 95% confidence interval [95% CI] of +0.1 minutes [95% CI = -0.6 to 0.8]), rod-intercept-time (-0.23 minutes [95% CI = -1.38 to 0.93]), and S2 slope (-0.01 LogUnits/minutes [95% CI = -0.02 to -0.01]). In the retest reliability-substudy (N = 10 participants, 32.0 years [95% CI = 27.0, 57.5]), the corresponding LoAs were (cone-rod-break-time) -3.94 to 2.78 minutes, (rod-intercept-time) -4.55 to 3.11 minutes, and (S2 slope [rate-limited component of rod recovery]) -0.03 to 0.03 LogUnits/minutes. The LoAs for the steady-state cone and rod thresholds were -0.28 to 0.33 LogUnits and -0.34 to 0.28 LogUnits. The devised fundus-controlled dark-adaptometry method yields valid and reliable results. Fundus-controlled dark-adaptometry solves the critical need for localized testing of the visual cycle and retinoid transfer in eyes with unstable fixation.

A 3D-printed oscillatory polymerase chain reaction system using a single heater

Nucleic acid amplification is a process that arises in all organisms. Polymerase chain reaction (PCR) is a standard laboratory nucleic acids amplification technology in vitro widely used in medical detection and biometric-related research. This device represents the first demonstration of 3D-printed oscillatory thermocycling within a portable PCR device. This study uses a single Peltier element to create the three temperature regions required for PCR. The overall control system uses an Arduino microcontroller with the advantages of easy development, abundant resources, and easy access to device software design. The sample-driving system integrated with the 3D printed object and the laser-cut acrylic component to achieve the linear reciprocating motion of the sample is conducive to the realisation of the portable design of the device. After system optimisation, the reaction tube moves from the denaturation zone to the aluminum block for cooling, and it moves to the groove of the denaturation zone from the annealing zone to accelerate the sample temperature rise. The time duration reduces from 180 s to 85 s to complete one thermal cycle. Combining the 5 mm instead of 3 mm thickness of the acrylic material and the configuration of the acrylic end exposed in the air creates a more significant temperature difference from 25 °C to 40 °C. Using this oscillatory PCR device, we successfully amplified a 219 bp PCR product of turmeric DNA after 30 thermal cycles within 50 min. The developed PCR device will cost less than $300 USD in our work. Compared with previous works, the cost of this research device is relatively low, and the total volume of the device is quite small. This work should be interesting to persons involved in diagnosing the infectious diseases of viruses and bacteria and identifying the fake healthy food in the low-cost bioreaction chambers in fields and remote areas.

P123: Cognitive Disorders and Impact on Caregivers: The COGCARE Study protocol

Introduction and objectives:Dementia is associated with a high burden of disease, impacting patients, families and society. Nevertheless, related epidemiological data is becoming outdated, given the difficulties of implementing costly and laborious fieldwork surveys. Data is also difficult to retrieve from health and social services’ information systems. Overall, we must improve the feasibility and validity of case definition regarding dementia and the assessment of caregivers’ consequences. The 10/66 Dementia Research Group diagnostic algorithm is a cross-culturally valid method(1). A ‘short 10/66’ was also validated(2), but not in Portugal. We intend to assess its feasibility and validity in Portuguese samples, using REDcap (a browser-based, metadata-driven software) in mobile devices. Additionally, we aim to assess dementia family caregivers’ subjective burden and psychological distress, contrasting primary care and hospital outpatient settings.Methods:A multicentre mixed-methods study will be conducted on fifty dyads of older people with dementia and their caregivers, plus 150 dyads of ‘controls’ and their close family members, as informants. The ‘short 10/66’ will be administered. Dementia caregivers’ assessments include the Zarit Burden Interview and Self-report Questionnaire. Quantitative analyses will estimate the sensitivity and specificity of the ‘short 10/66’ dementia case definition. Semi-structured qualitative interviews will be conducted with participants and research assistants, exploring their experiences with the assessment process; thematic analysis will then be used.Implications:We expect this study to facilitate the diagnosis of dementia and data collection in health/social services on a routine basis, which will potentially improve the feasibility and decrease the costs of epidemiological surveys and allow for prevalence monitoring in Portugal.

Цифровая трансформация промышленных предприятий и экономический рост

The article examines the features of the digital transformation of industrial enterprises in the Russian Federation at the present stage of their development. The empirical part of the study is focused on examining the growth rates of large enterprises representing various sectors of the country’s national economy (EvrazHolding, Sukhoi Company, and Gazprom Neft) and their transformation as part of the digitalization of the domestic economic system. Their benefits from the introduction of digital technologies into production processes, including ecosystem purposes, which simplify the process of selling products without loss of quality, ensuring a high level of cooperation between platform participants and high market transparency, are considered. The indicators of profitability and business activity of the above companies (2017–2021) were studied as part of the digital transformation of these enterprises. The problems of introducing digital technologies are identified: high risks of data leakage when introducing online technologies; high costs of introducing digital technologies; changes in the labor market; customer privacy costs; errors in device operation, calculations, and software. In accordance with the above-identified problems, ways to resolve them have been developed based on a strategic approach to the digital transformation of the financial and economic activities of enterprises: preparation of production in accordance with the requirements of the digital passport of the Ministry of Industry and Trade; drawing up an investment schedule; preparation of a business process reengineering schedule; determination of strategic directions for R&amp;D to improve the quality of products and increase labor productivity through digital technologies.

Security and Privacy Issues in Internet of Things (IoT) Devices Using COPRAS Method

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

Automated Deployment of Medical Device Software Using Ansible and Jenkins

Automated Deployment of Medical Device Software Using Ansible and Jenkins

Determinatioan Of Sediment Thickness By Using Microtremor Data In Kadia Sub-District, Kendari City

This study aims to determine the value of the distribution of sediment layer thickness. This research was conducted in the Kadia District area, Kendari City, with data collection carried out at 16 measurement points with intervals for each point varying from 400 to 600 meters with a sampling frequency of 100 Hz. The data obtained from this study is in the form of ground vibration data which is then downloaded software DataPro.exe East-Westhorizontal component, the North-South vertical component Up-Down. The downloaded microtremor data is then processed using a device software Geopsy 4.3.2 to obtain the H/V curve. The dominant frequency value (f0) is obtained from the H/V curve with a dominant frequency range of 1.247 Hz to 4.955 Hz and the shear (VS30) is obtained from the website www.USGS.gov with a VS30 of 276 m/s up to 301 m/s. Based on the data that has been obtained, the distribution value of the sediment layer thickness (h) will be determined. The value of the thickness of the sediment layer (h) in the study area ranged from 13,016 meters to 59,473 meters.

Multimodal Gamma Stimulation Improves Activity but not Memory in Aged Tgf344-AD Rats.

Multimodal sensory gamma stimulation is a treatment approach for Alzheimers disease that has been shown to improve pathology and memory in transgenic mouse models of Alzheimer's. Because rats are closer to humans in evolution, we tested the hypothesis that the transgenic rat line bearing human APP and PS1, line TgF344-AD, would be a good supplemental candidate to test the efficacy of this treatment. Current therapy approaches under investigation seek to utilize the immune response to minimize or degrade the accumulation of β-amyloid plaque load in mouse models designed to overexpress Aβ. However, many of these models lack some of the hallmarks of Alzheimer's disease, such as hyperphosphorylated tau and neuronal cell loss. The TgF344-AD transgenic rat model is a good candidate to bridge the gap between mouse models and clinical efficacy in humans. The objective of this study was to use multimodal gamma stimulation at light and auditory modalities simultaneously to test whether this enhances memory performance as measured by the object location task and the spontaneous alternation task. In our study, we designed and built a low-cost, easy-to-construct multimodal light and sound gamma stimulator. Our gamma stimulation device was built using an Arduino microcontroller, which drives lights and a speaker at the gamma frequency. We have included in this paper our device's parts, hardware design, and software architecture for easy reproducibility. We then performed an experiment to test the effect of multimodal gamma stimulation on the cognitive performance of fourteen-month-old TgF344-AD rats. Rats were randomly assigned to either an experimental group that received gamma stimulation or a control group that did not. Performance in a Novel Object Location (NOL) task and spontaneous alternation task was evaluated in both groups before and after the treatment. Multimodal gamma stimulation did not improve memory compared to unstimulated TgF344-AD rats. However, the gamma-stimulated rats did spend significantly more time exploring objects in the novel location task than the unstimulated rats. In the spontaneous alternation task, gamma-stimulated rats exhibited significantly greater exploratory activity than unstimulated controls. Multimodal gamma stimulation did not enhance memory performance in the object location task or the spontaneous alternation task. However, in both tasks, the treatment group had improved measures of exploratory activity relative to the untreated group. We conclude that several limitations could have contributed to this mixed effect, including aging complications, different animal models, or light cycle effects.

A13Mixed reality simulation training in the assessment and management of acutely unwell patients in undergraduate medical education: a pilot study

Development and enrichment of the undergraduate simulation experience is needed due to its utility in developing the aspiring clinician and their ability to assess and manage acutely unwell patients. However, with its heavy resource demand, and the desire for technologically enhanced learning by ‘Generation Z’, novel simulation modalities must now be considered [1]. Mixed reality (MR) technology provides a platform to deliver such simulation training; however, it is yet to be studied in this context. To evaluate this, we conducted an observational cohort study to assess if MR simulation improves students’ perceived ability to assess and manage acutely unwell patients. We recruited 32 undergraduate medical students and asked them to rate their ability to assess and manage acutely unwell patients out of 10, before delivering a 2-hour MR simulation teaching session using the Microsoft HoloLens head-mounted device and HoloScenario software produced by GigXR [2]. They were then asked again to rate their assessment and management ability alongside their experience of the MR system and its usability. Data were analysed using paired Analysis of the MR teaching session showed a statistically significant improvement of student scores in their perceived ability in assessment ( MR shows promise in its ability to deliver simulation training and improve students’ perceived ability to assess and manage acutely unwell patients. Advances in software availability and simulation exercises are required for complete integration into undergraduate medical curricula. Further research is required to assess if MR simulation objectively improves student performance in this area. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

Enhanced hole injection in Ga-polar 290 nm AlGaN-based DUV LEDs with a p-n junction hole accelerator

The limited kinetic energy of holes in AlGaN-based deep ultraviolet light-emitting diodes (DUV LEDs) poses a challenge in their transportation into the active region across the Al-rich electron blocking layer (EBL) and significantly restricts the electrical and optical performance of DUV LEDs. In this work, we propose a hole accelerator structure composing a p-AlxGa1-xN/n-AlxGa1-xN junction to improve the hole injection efficiency and explore the mechanism behind the enhanced performance with the Advanced Physical Models of Semiconductor Devices software (APSYS). The built-in electric field of the p-n junction distributes along the [000-1] direction, which can enhance the hole drift velocity and improve the hole injection into the active region. Moreover, with an optimum Al composition of 50%, [000-1] oriented polarization-induced electric field can be generated at the vicinity of both the p-EBL/accelerator and accelerator/hole supplier interfaces, which further boosts the holes into the active region. Besides, the original steep barrier for holes at the EBL/hole supplier interface can be splited into a two-step barrier which is more favorable for hole transportation. As a result, an enhanced optical power by 49.4% and alleviated efficiency droop by 76.3% can be achieved with the proposed p-n junction-based hole accelerator. The results can pave the way for AlGaN-based DUV LEDs towards high-power and high-efficiency applications.

Medical Device Software: From Requirements to Certification.

The role of software in healthcare is getting more and more pervasive. Nevertheless, manufacturers sometimes forget that these software are medical devices and must be certified according to the EU Medical Device Regulation 2017/745. In this work we propose a pipeline for developing a Medical Device Software (MDS) compliant with the regulations and certifiable. The pipeline includes the phase of requirements elicitation, risk assessment and analysis of effectiveness as key elements. The preparation of the technical file should be carried out in parallel with the MDS development. In the overall, it can be stated that the certification process starts with the conceptualization of the MDS and proceeds all along its design and implementation.

Fault detection system of subway sliding plug door based on adaptive EMD method

With the rapid development of urban rail transit, the safety of subway sliding plug doors has become a great concern. To improve the operational reliability of the sliding plug door, we developed a fault detection system based on the adaptive empirical mode decomposition (AEMD). Firstly, we designed a hardware acquisition device and analysis software to collect motor current signal data during the opening and closing of the sliding plug door. Secondly, to address the impact of noise on signal analysis, the AEMD denoising method is proposed. This method employs EMD to obtain intrinsic mode functions (IMFs), and select the appropriate IMF components for reconstruction based on the adaptive threshold of Hausdorff distance, resulting in improved denoising effectiveness. Thirdly, waveform segments of different faults are sliced to reduce the amount of computation and effectively improve recognition accuracy. Meanwhile, this paper utilizes feature selection methods and machine learning techniques to classify the 12 subway sliding plug door faults. It is worth noting that most of these faults have not been extensively studied in previous classification research. The experimental results show that the identification accuracy reaches 98.96% on the practical platform. Moreover, the effectiveness and robustness of our proposed method are further validated through practical tests, ablation experiments, and comparisons with other relevant literature.