Impact Signal Analysis and Fault Diagnosis of High-Speed Automata

2011 ◽  
Vol 108 ◽  
pp. 95-100
Author(s):  
Ming Zhi Pan ◽  
Hong Xia Pan ◽  
Run Peng Zhao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Signal Analysis ◽  
High Speed ◽  
Theoretical Research ◽  
Experiment Study ◽  
Characteristic Parameters ◽  
Pattern Decomposition ◽  
Time Diagnosis

Aiming at the early fault diagnosis of the automata of small-caliber antiaircraft guns, a full set of theories and techniques for the early fault diagnosis of the high-speed automata, based on the techniques of the decomposition of motility patterns and the fusion of information entropy, have been established in this article by the strategy of combining of the theoretical research and the experiment study, which can deal with the practical problems of the superposition and interference of successive impulse signals, cope with the difficulties in the effective extraction of the weak signals of early faults in the environment with strong noises and in the determination of sensitive characteristic parameters, and overcome the disadvantages of the traditional methods of fault diagnosis such as its low conversation speed, the weakness in determining the accurate location of faults and the impotence in real time diagnosis. With the techniques of motility pattern decomposition and information entropy fusion, the efficiency and accuracy of the early fault diagnosis of automata will be increased and the study scope of the mechanical fault diagnosis discipline will be extended.

Automaton Fault Diagnosis Based on Motion Morphology and Information Entropy

2011 ◽  
Vol 121-126 ◽  
pp. 3175-3179
Author(s):  
Ming Zhi Pan ◽  
Hong Xia Pan ◽  
Run Peng Zhao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Theoretical Research ◽  
Practical Application ◽  
Transient Vibration ◽  
New Ideas ◽  
Weak Fault ◽  
Short Time ◽  
Time Diagnosis ◽  
General Concern

Online monitoring and fault diagnosis is an important link of guaranteeing the equipment smooth operation and reliable working, which receives general concern. This subject uses the strategy of combining theoretical research and experimental research, and establishes a set of automaton fault diagnosis theory and method based on motion morphology and information entropy. It solves the following problems, the weak fault signals in the short-time and transient vibration response signals are easy to be drowned in practical application, effective and sensitive characteristic parameters are difficult to be extracted, accurate positioning of fault and real-time diagnosis are difficult to be realized. Use the motion morphology and information entropy to put forward new ideas and methods for the short-time and transient vibration signals analysis processing and feature extraction, and is applied in artillery automaton field, which expands the research scope of mechanical fault diagnosis subject.

Sampling, Preservation and Counting of Samples II: Zooplankton

2017 ◽  
Author(s):  
Peter H. Wiebe ◽  
Ann Bucklin ◽  
Mark Benfield
Keyword(s):  
Genetic Analysis ◽  
High Speed ◽  
Morphological Analysis ◽  
Taxonomic Composition ◽  
Sampling Techniques ◽  
Sample Analysis ◽  
Sample Preservation ◽  
The Past ◽  
Plankton Collection

This chapter reviews traditional and new zooplankton sampling techniques, sample preservation, and sample analysis, and provides the sources where in-depth discussion of these topics is addressed. The net systems that have been developed over the past 100+ years, many of which are still in use today, can be categorized into eight groups: non-opening/closing nets, simple opening/closing nets, high-speed samplers, neuston samplers, planktobenthos plankton nets, closing cod-end samplers, multiple net systems, and moored plankton collection systems. Methods of sample preservation include preservation for sample enumeration and taxonomic morphological analysis, and preservation of samples for genetic analysis. Methods of analysis of zooplankton samples include determination of biomass, taxonomic composition, and size by traditional methods; and genetic analysis of zooplankton samples.

High-speed electro-optic switch using buried electrode structure in polymer Mach–Zehnder waveguide

2016 ◽  
Vol 30 (06) ◽  
pp. 1650063 ◽  
Author(s):  
Jingwen Sun ◽  
Jian Sun ◽  
Yunji Yi ◽  
Lucheng Qv ◽  
Shiqi Sun ◽  
...  
Keyword(s):  
Insertion Loss ◽  
Rise Time ◽  
High Speed ◽  
Low Cost ◽  
Characteristic Parameters ◽  
Electrode Structure ◽  
Processing Cost ◽  
Electro Optic ◽  
Disperse Red 1 ◽  
Excellent Photostability

A low-cost and high-speed electro-optic (EO) switch using the guest–host EO material Disperse Red 1/Polymethyl Methacrylate (DR1/PMMA) was designed and fabricated. The DR1/PMMA material presented a low processing cost, an excellent photostability and a large EO coefficient of 13.1 pm/V. To improve the performance of the switch, the in-plane buried electrode structure was introduced in the polymer Mach–Zehnder waveguide to improve the poling and modulating efficiency. The characteristic parameters of the waveguide and the electrodes were carefully designed and the fabrication process was strictly controlled. Under 1550 nm, the insertion loss of the device was 12.7 dB. The measured switching rise time and fall time of the switch were 50.00 ns and 54.29 ns, respectively.

Fault diagnosis of railway freight car wheelset based on deep belief network and cuckoo search algorithm

2021 ◽  
pp. 095440972110291
Author(s):  
Honghui Li ◽  
Hongkun Wang ◽  
Ziwen Xie ◽  
Mengqi He
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Deep Belief Network ◽  
Belief Network ◽  
Railway Freight ◽  
Hybrid Simulated Annealing ◽  
Technical Issues

As the key running part of the railway freight transportation system, the wheel not only bears the load of the vehicle, but also ensures the running and steering of the car body on the rails. The frequent high-speed friction with the rail and brake is the main reason for early failure of wheelset tread. Therefore, real-time status monitoring and early fault diagnosis of wheel treads have become key technical issues that must be solved in the reform of the railway freight maintenance system. In this paper, an adaptive hybrid Simulated Annealing Cuckoo Search algorithm (SA-ACS) is proposed and applied to the Deep Belief Network (DBN). The SA-ACS-DBN algorithm is used to improve the training speed and convergence accuracy of the diagnosis model. Finally, it is found through the comparison experiment of wheel tread fault data that the data results prove the feasibility of the SA-ACS-DBN model in the application of wheelset fault diagnosis.

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

scholarly journals Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3609
Author(s):  
Mykola Sysyn ◽  
Michal Przybylowicz ◽  
Olga Nabochenko ◽  
Lei Kou
Keyword(s):  
High Speed ◽  
Experimental Studies ◽  
Dynamic Interaction ◽  
Theoretical Research ◽  
Rolling Stock ◽  
Monitoring Methods ◽  
Beam Dynamic ◽  
Track Geometry ◽  
Data Driven Approach ◽  
Support Conditions

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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