Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

Wang, Yongchao; Meng, Dawei; Li, Ran; Zhou, Yongqin; Zhang, Xiaoyu

doi:10.1109/access.2021.3051986

Cited by 12 publications

(3 citation statements)

References 44 publications

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“…The IMM approach, originally proposed in [6], is a suboptimal hybrid estimator which consists of a bank of multiple KFs, each of them matched on a specific target model, that tracks the target motion through a weighted average KFs estimation using a probability model. To exploit its potentiality, many studies have been carried out on tracking estimation through IMM, ranging from the classification of dynamical characteristics of different targets such as drones, jets, and civil aircrafts (see references in [3]) to the electric vehicles for the stable steering control problem [7] until to the multifault diagnosis of lithium-ion batteries in [8]. Focusing on the fast missile tracking problem, different IMM strategies have been proposed in the technical literature.…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆

Lui

Tartaglione

Conti³

et al. 2023

IEEE Access

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Due to its extensive applications in different contexts, moving target tracking has become a hot topic in the last years, above all in the military field. Specifically, missile tracking research received a great effort, mainly for its importance in terms of security and safety. Herein, traditional solutions, e.g. Interacting Multiple Model (IMM) based on the Kalman estimation theory, achieve good performance under the main restrictive assumption of the a priori knowledge of the target model, so neglecting the unavoidable presence of model uncertainties and limiting the achievable tracking accuracy only by the presence of the measurement noise. With the specific aim of overcoming this narrowness, this work investigates the capability of deep neural networks in predicting the missile maneuvering trajectories in a model-free fashion. The idea is to leverage the Long-Short Term Memory (LSTM) net due to its excellent capability in learning long-term dependencies of temporal information. Two different LSTM-based architectures have been hence designed to predict both position and velocity of a missile using raw and noisy measurements provided by a realistic radar system, exploiting a large database abundant of realistic off-line data. Training results and theoretical derivations are verified through non-trivial scenarios in order to assess the capability of predicting unknown and realistic 3D missile maneuvers. Finally, the proposed approach has been also compared with a performing model-based IMM algorithm, suitably tuned to deal with realistic missile maneuvers, confirming the excellent generalization abilities of the developed data-driven architectures for different datasets.

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Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆

Lui

Tartaglione

Conti³

et al. 2023

IEEE Access

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“…The IMM algorithm is introduced into the condition monitoring and fault diagnosis of railway vehicles to visually separate the fault location and reduce the probability of fault misjudgment [ 37 ]. Besides, an IMM based on low inertia and anti-noise (LN-IMM) is proposed [ 38 ]. The multi-fault diagnosis of lithium-ion batteries can be realized by combining with strong tracking Kalman filter (STKF).…”

Section: Introductionmentioning

confidence: 99%

Interactive multi-model fault diagnosis method of switched reluctance motor based on low delay anti-interference

Zhou

Wang

et al. 2023

PLoS ONE

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Given fault false alarm and fault control failure caused by the decrease of fault identification accuracy and fault delay of Switched Reluctance Motor (SRM) power converter in complex working conditions, a method based on the Interactive Multi-Model (IMM) algorithm was proposed in this paper. Besides, the corresponding equivalent circuit models were established according to the different working states of the SRM power converter. The Kalman filter was employed to estimate the state of the model, and the fault detection and location were realized depending on the residual signal. Additionally, a transition probability correction function of the IMM was constructed using the difference of the n-th order to suppress the influence of external disturbance on the fault diagnosis accuracy. Concurrently, a model jump threshold was introduced to reduce delay when the matched model was switched, so as to realize the rapid separation of faults and effective fault control. The simulation and experiment results demonstrate that the IMM algorithm based on low delay anti-interference can effectively reduce the influence of complex working conditions, improve the anti-interference ability of SRM power converter fault diagnosis, and identify fault information accurately and quickly.

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“…With the improvement of human living environment requirements, countries worldwide have paid great attention to the deterioration of the global environment and climate warming. The zero-emission electric vehicle industry has become an essential field of competition and development among countries, and battery-driven electric vehicles and hybrid electric vehicles have entered a period of rapid evolution [1][2][3][4]. As the energy storage carrier of electric cars, battery safety, and reliability significantly affect the performance of electric vehicles [5].…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Yao

Xiao

et al. 2022

IEEE Access

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The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

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Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

Cited by 12 publications

References 44 publications

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆

Interactive multi-model fault diagnosis method of switched reluctance motor based on low delay anti-interference

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

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Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

Cited by 12 publications

References 44 publications

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction⋆

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction⋆

Interactive multi-model fault diagnosis method of switched reluctance motor based on low delay anti-interference

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

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Product

Resources

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Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆

Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction^⋆