Health monitoring and fault prediction using a lightweight deep convolutional neural network optimized by Levy flight optimization algorithm

Rajakumar, M. P.; Ramya, J.; Maheswari, B. Uma

doi:10.1007/s00521-021-05892-0

Cited by 10 publications

(6 citation statements)

References 38 publications

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“…Several studies have utilized Machine Learning models and graphical user interfaces to identify and visualize vehicle failures, making it easier for users to track data and identify hidden patterns and trends. In one study [52], a lightweight deep learning model was developed and deployed during manufacturing operations, where alarms generated were transmitted through a WiFi network. A smartphone application was also used to monitor noise near wearable components of the vehicle.…”

Section: Failure Analysis and Interfacementioning

confidence: 99%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

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The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

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Section: Failure Analysis and Interfacementioning

confidence: 99%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

View full text Add to dashboard Cite

show abstract

“…In [ 60 ], Rajakumar et al proposed a framework to identify the health condition of the vehicles. They design a fault-detection algorithm by using a deep convolutional neural network (DCNN) on smartphones.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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“…Implementing the LFOA algorithm makes it feasible to create a lightweight DCNN model suitable for implementation on edge processors like smartphones. Experimental results demonstrate that the suggested model enhances the accuracy of classifying the six faults to be identified, making it an effective research model for determining the health state of cars [ 30 ]. The novelty of this research resides in the fact that it proposes a novel AOC–ResNet50 network and its successful use in wind turbine defect detection.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

Raju

Ramaswamy

Eid

et al. 2023

Sensors

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Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method’s success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component’s benefits to enhance the predictive model’s overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

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Health monitoring and fault prediction using a lightweight deep convolutional neural network optimized by Levy flight optimization algorithm

Cited by 10 publications

References 38 publications

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

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