Deep learning approaches for multi-modal sensor data analysis and abnormality detection

被引：0
作者：
Jadhav, Santosh Pandurang [1 ]
Srinivas, Angalkuditi [2 ]
Dipak Raghunath, Patil [3 ]
Ramkumar Prabhu, M. [4 ]
Suryawanshi, Jaya [1 ]
Haldorai, Anandakumar [5 ]
机构：
[1] Department of Information Technology, MVPS ‘KBT College of Engineering, Maharashtra, Nashik
[2] Department of Computer Science Applications, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram
[3] Department of Computer Engineering, Amrutvahini College of Engineering, Maharashtra, Sangamner
[4] Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai
[5] Center for Future Networks and Digital Twin, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore
来源：
Measurement: Sensors | / 33卷
关键词：
Abnormality detection; Data integration and anomaly detection; Deep learning; Feature extraction; Multi-modal sensor data; Neural networks; Sensor fusion;
D O I：
10.1016/j.measen.2024.101157
中图分类号：
学科分类号：
摘要：
The information gathered within the structure health monitored (SHM) device would display a range of irregularities mainly a result of sensing defeat, noise disturbance, and different causes. This will greatly impair the structure's security evaluation. This research presents a multipurpose deeper neural network-based data-driven abnormality diagnostic system called SHM. The multipurpose deeper neural networks fuse single-dimensional as well as two-dimensional properties regarding the sensory signals to increase the detecting efficiency. Two separate Convolutional Neural Network, streams within the system are used for obtaining time-frequency characteristics from information collected by sensors (also referred to as two-dimensional-CNN medium) as well as unprocessed one-dimensional characteristics (also referred to as one-dimensional-CNN medium). Following the 2D as well as 1D streams' individual clustering and filtering processes using the sensing information, the two categories of recovered properties have been distorted through single-dimensional matrices that combined within the fusion level. The ideal framework shows the efficacy as well as potential of the suggested approach having a precision percentage of 95.10%. Considering an accurate AI-assisted electronic instrument for evaluating security in structured health management networks, the suggested approach has an exciting period ahead of it. © 2024 The Authors
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