Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica's global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam.
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http://dx.doi.org/10.3390/s18010068 | DOI Listing |
ISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
View Article and Find Full Text PDFPLoS One
January 2025
Automation School Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.
Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies.
View Article and Find Full Text PDFCondition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data analytics, machine learning (ML), Industry 4.0, and Internet of Things (IoT) applications.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou, 510640, China; Institute for Super Robotics (Huangpu), Guangzhou, 510555, China; Nanchang University, Nanchang, 330031, China; College of Computer Science and Engineering, Jishou University, Jishou, 416000, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China; School of Electronical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China; School of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China; Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China. Electronic address:
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model.
View Article and Find Full Text PDFMath Biosci Eng
December 2024
School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, China.
As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM).
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