State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement

Xu, Xiaobin; Li, Zhenghui; Guo, Li; Zhou, Zhe

doi:10.3390/s17040924

Cited by 7 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Certainly, the proposed method also inherits good properties of the original detector including high insensitivity to parasitic reflections, simple and inexpensive hardware realization, etc. At the same time, it is worth noting that, the proposed method can be further united with the error compensators in [12] and [25] to further improve the measurement accuracy because the latter are fit for the intact RF sequences.…”

Section: Discussionmentioning

confidence: 99%

Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection

Fang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Acoustic resonance-based level measurement principle needs to extract a sequence of resonance frequencies (RFs) from the synthesis wave and then calculate level height via this RF sequence. However, in practice, the uncertain disturbances in the measurement environment usually lead to the signal distortion of the collected synthesis wave. In this case, some RF points in the sequence are inevitably missed which causes the nonnegligible calculation error. Hence, based on the Dempster-Shafer evidence theory (DST), this paper presents a recursive evidence fusion method to combine multiple RF sequences in a row. It provides a natural way to supplement the missed RF points and also significantly improve the measurement accuracy even if the observed RF sequences are all intact. That is to say, regardless of the missing case or intact case, the proposed fusion method always has high performance. Finally, the comparative experiments of level detection show the level gauge using this method is robust for the sequence with the missing RF points and can further provide higher measurement accuracy than the single RF sequence-based and digital filteringbased level detection methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection

Fang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In ER Theory, it requires two pieces of evidence to be independent of each other, but evidence dependence often exist in practice. Ref [8] pointed out that if the evidences are derived from multiple independent information sources, when different evidence uses the same information source, these evidences are often mutually dependent.The energy En(E) is expressed as:…”

Section: Basic Concepts In Dependent Evidence Fusionmentioning

confidence: 99%

“…The synthesized sound wave collected by the microphone is shown in Figure 4, and 14 resonance frequency points are generated in total. Then, the frequency and wavelength corresponding to the resonance frequency point are extracted in the amplitude detection through fast Fourier transform and smoothing filter [8,9] , as shown in Figure 5. Obviously, because the synthesized sound wave is influenced by external noise and sound wave attenuation, the missed detection phenomenon occurs in the extraction process of resonance frequency point.…”

Section: Fig 3: Experiments Set-upmentioning

confidence: 99%

A novel Acoustic Fluid Level Estimation Method Based on Evidence Fusion Mechanism

Li¹

2021

converter

View full text Add to dashboard Cite

In order to reduce the missed detection error and the systematic error caused by acoustic resonance fluid level detection, liquid level estimation method based on evidence fusion mechanism is designed. It establishes a two dimensional dynamic system model of the standingwavelength. The state evidence of wavelength is obtained through the random set description of evidence, and the extension principle of random set is used to get the observation evidence ofwavelength. The evidential reasoning (ER) rule and dependent evidence fusion are used to fuse those evidence, and the estimation value of fluid level can be calculated from fused result based on pignistic expectation. The corresponding liquid level estimation experiment illustrates the validity and feasibilityof the proposed method.

show abstract

“…Dempster-Shafer evidence theory [ 34 , 35 ], also called Dempster-Shafer (D-S) theory or evidence theory, is a popular theory to deal with uncertain information [ 40 , 41 , 42 , 43 , 44 , 45 ]. Compared with traditional probability theory, this theory has an advantage of directly expressing the “uncertainty” by assigning the probability to the set composed of multiple objects, it therefore has attracted increasing interest in uncertainty reasoning and modelling [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ] and been further extended such as generalized evidence theory (GET) in open world [ 53 , 54 , 55 ].…”

Section: Preliminariesmentioning

confidence: 99%

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

Deng

Jiang

2017

Sensors

View full text Add to dashboard Cite

Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

show abstract

State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement

Cited by 7 publications

References 29 publications

Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection

Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection

A novel Acoustic Fluid Level Estimation Method Based on Evidence Fusion Mechanism

Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

Contact Info

Product

Resources

About