scholarly journals Analysis of the vibro-acoustic data from test rig -comparison of acoustic and vibrational methods

2021 ◽  
Vol 942 (1) ◽  
pp. 012017
Author(s):  
Paweł Zimroz ◽  
Hamid Shiri ◽  
Jacek Wodecki
Keyword(s):  
Underground Mining ◽  
Research Effort ◽  
Industrial Processes ◽  
Remote Measurement ◽  
Test Rig ◽  
Short Term ◽  
Vibration Signals ◽  
Acoustic Data ◽  
Novel Approach ◽  
Contact Sensors

Abstract Detection of damage is a significant issue in providing efficiency and safety in industrial processes. In underground mining much research effort is made for developing an automatic system of diagnosing the machinery using robots. One of the major groups of equipment utilized and maintained in the mines is rotating machinery. Local damage occurring in such machines commonly have a cyclostationary character in short term as any change in their characteristics is expected to repeat periodically. In most cases they can be easily detected based on vibration signals measured with contact sensors (accelerometers). However if mobile robots such as UAV (unmanned aerial vehicles) are planned to be used, remote measurement is firmly preferred. In this paper we compare vibrational detection with a novel approach based on analysing an acoustic signal recorded by a microphone.

scholarly journals A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4493
Author(s):  
Rui Silva ◽  
António Araújo
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Recurrent Neural Networks ◽  
Cutting Process ◽  
Time Dependent ◽  
Industrial Processes ◽  
Fundamental Part ◽  
Novel Approach ◽  
Noisy Signals ◽  
Proper Condition

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

scholarly journals Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinyu Wang ◽  
Kegui Chen ◽  
Xueping Tan
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Fuzzy Information ◽  
Information Distribution ◽  
Short Term ◽  
Distribution Method ◽  
Price Changes ◽  
Spot Prices ◽  
Price Series ◽  
Novel Approach

This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method statistically outperforms the benchmark of logistic regression model in prediction accuracy. The results indicate that this new approach is effective in direction accuracy.

scholarly journals Understanding beverage taxation: Perspective on the Philadelphia Beverage Tax’s novel approach

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Ryan M. Kane ◽  
Vasanti S. Malik
Keyword(s):  
Policy Process ◽  
Empirical Studies ◽  
Health Impact ◽  
Short Term ◽  
Sugar Sweetened Beverage ◽  
Novel Approach ◽  
Global Trend ◽  
Health Impact Assessments ◽  
Political Entrepreneurs

Despite the growing global trend of sugar-sweetened beverage (SSB) taxes for their potential as an untapped source of revenue and as a public health boon, these legislative efforts remain controversial. Multiple articles have reviewed this trend in recent years from modeling of long-term impacts to short-term empirical studies, yet most comprehensive, long-term health impact assessments remain forthcoming. These multi-faceted efficacy studies combined with case-based assessments of the policy process, descriptive pieces highlighting unique features of the policy and reflective perspectives targeting unanswered questions create a comprehensive body of literature to help inform present and future legislative efforts. The passage of the Philadelphia Beverage tax required a mix of political entrepreneurs, timing and context; while uniquely employing a nonpublic health frame, specific earmarking and a broadened scope with the inclusion of diet beverages. This perspective on the Philadelphia Beverage Tax will describe the passage and novel features of the Philadelphia Beverage Tax with a discussion of the ethical questions unique to this case.

The overlap of aftershock coda waves and forecasting the first hour aftershocks

2020 ◽  
Author(s):  
Eugenio Lippiello ◽  
Giuseppe Petrillo ◽  
Cataldo Godano ◽  
Lucilla de Arcangelis ◽  
Anna Tramelli ◽  
...  
Keyword(s):  
Aftershock Sequence ◽  
Coda Waves ◽  
Statistical Features ◽  
Short Term ◽  
Geophysical Research ◽  
Ground Shaking ◽  
Epidemic Type Aftershock Sequence ◽  
Novel Approach ◽  
Single Station ◽  
Strong Ground

<p>We show that short term post-seismic incompleteness can be interpreted in terms of the overlap of aftershock coda waves. We use this information to develop a novel procedure which gives accurate occurrence probabilities of post-seismic strong ground shaking within 30 minutes after the mainshock. This novel approach uses, as only information, the ground velocity recorded at a single station without requiring that signals are transferred and elaborated by operational units. We will also discuss how this information can be implemented in the Epidemic-Type Aftershock Sequence model in order to reproduce statistical features in time and magnitude of recorded aftershocks.</p><p><strong>Main references </strong></p><p>de Arcangelis L., Godano C. & Lippiello E. (2018) <em>The Overlap of Aftershock Coda Waves and Short-Term Postseismic Forecasting. </em><strong>Journal of Geophysical Research: Solid Earth, </strong>123: 5661-5674,doi:10.1029/2018JB015518</p><p>Lippiello E., Petrillo G. , Godano G. , Tramelli A., Papadimitriou E. &, Karakostas V. (2019)<em> Forecasting of the first hour aftershocks by means of the perceived magnitude. </em><strong>Nature Communications</strong> , 10, 2953, doi:10.1038/s41467-019-10763-3</p>

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