Hybrid method of intellectual diagnosis and forecasting of complex technical systems

2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 78-87
Author(s):  
Vorobiov A ◽  
◽  
Zakusylo P ◽  
Kozachuk V ◽  
◽  
...  
Keyword(s):  
Hybrid Method ◽  
Technical Condition ◽  
Forecast Errors ◽  
Diagnostic Systems ◽  
Current Time ◽  
Real Model ◽  
Internal Parameters ◽  
Failure State ◽  
Long Term Forecasting ◽  
Short Term Forecasting

Modern control and diagnostic systems (CDS) usually determine only the technical condition (TC) at the current time, ie the CDS answers the question: a complex technical system (CTS) should be considered operational or not, and may provide little information on performance CTS even in the near future. Therefore, the existing scenarios of CDS operation do not provide for the assessment of the possibility of gradual failures, ie there is no forecasting of the technical condition. The processes of parameter degradation and degradation prediction are stochastic processes, the “behavior” of which is influenced by a combination of external and internal factors, so the deg-radation process can be described as a function that depends on changes in the internal parameters of CTS. The hybrid method involves the following steps. The first is to determine the set of initial characteristics that characterize the CTS vehicle. The second is the establishment of precautionary tolerances of degradation values of the characteristics that characterize the pre-failure technical con-dition of the CTS. The third is to determine the rational composition of informative indicators, which maximally determine the "behavior" of the initial characteristics. The fourth — implementa-tion of multiparameter monitoring, fixation of values of the controlled characteristics, formation of an information array of values of characteristics. Fifth — the adoption of a general model of the process of changing the characteristics of the CTS. Sixth — the formation of a real model of the process of changing the characteristics of Y(t) on the basis of an information array of values of char-acteristics obtained by multi-parameter monitoring. Seventh — forecasting the time of possible oc-currence of the pre-failure state of the CTS, which is carried out by extrapolating the obtained real model of the process of changing the characteristics of Y(t). It is proposed to use two types of mod-els: for medium- and long-term forecasting - polynomial models, for short-term forecasting — a lin-ear extrapolation model. At the final stage, forecast errors are determined for all types of models of degradation of pa-rameters and characteristics. Based on the results of the forecast verification, the models are adjust-ed

Application of fiber-optic measuring technology and phase-chronometric method for control and monitoring of technical condition of aircraft structures

2021 ◽  
pp. 49-56
Author(s):  
Stanislav S. Khabarov ◽  
Alexander S. Komshin
Keyword(s):  
Fatigue Life ◽  
Fiber Optic ◽  
Technical Condition ◽  
Point Of View ◽  
Flight Performance ◽  
Diagnostic Systems ◽  
Safe Operation ◽  
Excess Reserves ◽  
The Creation ◽  
Original Approach

Problems of ensuring the safe operation of an aircraft from the point of view of the fatigue life of its structure are considered. The relevance of the creation and implementation of diagnostic systems for monitoring the technical condition of structures of complex technical objects is shown on the example of a helicopter. An original approach to the creation and implementation of complex systems for diagnostics and monitoring of the technical condition of complex technical objects is presented, combining fiber-optic measuring technology and phase-chronometric method. It is shown that the use of monitoring and diagnostic systems ensures the transition to operation based on the actual technical condition. The proposed approach makes it possible to increase the time between overhaul intervals and reduce excess reserves in terms of the reliability factors of structures, which increases the flight performance of aircraft.

scholarly journals Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4429
Author(s):  
Yury Nikitin ◽  
Pavol Božek ◽  
Jozef Peterka
Keyword(s):  
Fuzzy Inference ◽  
Technical Condition ◽  
Numerical Control ◽  
Cnc Machine ◽  
Diagnostic Systems ◽  
Mechatronic Systems ◽  
Inference System ◽  
Electric Drives ◽  
Electrical Drives ◽  
Linguistic Model

The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

An Efficient Non-Iterative Hybrid Method for Analyzing Train–Rail–Bridge Interaction Problems

2020 ◽  
pp. 2150029
Author(s):  
Kang Shi ◽  
Xuhui He ◽  
Yunfeng Zou ◽  
Zhi Zheng
Keyword(s):  
Hybrid Method ◽  
Computational Efficiency ◽  
High Speed ◽  
Low Frequency ◽  
Coupled Analysis ◽  
Time Step ◽  
Coupling Vibration ◽  
Current Time ◽  
Railway Bridges ◽  
Frequency Coupling

The dynamic interaction problem for the train–rail–bridge (TRB) systems presents a computational challenge, especially for the analysis of large-size TRB coupling systems. To address this issue, an efficient non-iterative hybrid method (NHM) is proposed. With this method, the integrated TRB system is divided into three subsystems, i.e. the train subsystem, the rail subsystem, and the bridge subsystem. Based on the individual subsystems, a multi-step[Formula: see text] technique is adopted in which a fine time step is used to analyze the high-frequency coupling vibration for the train and rail subsystems, and a coarse time step is adopted to calculate the low-frequency coupling vibration for the rail and bridge subsystem. Additionally, Zhais explicit integral method is used to predict the displacement of the wheelsets and the rail at the current time step before using the Newmark method. The proposed method incorporates the advantages of Zhai’s explicit method and the MS technique to avoid the iteration that may be required for the train–rail coupled analysis. The simulation fidelity and computational efficiency of the proposed method are demonstrated in the analysis of two examples of typical high-speed railway bridges. It was demonstrated that the proposed method can significantly enhance the computational efficiency, while maintaining a higher precision with a larger time step in comparison with other existing methods.

scholarly journals Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

2019 ◽  
Vol 9 (4) ◽  
pp. 4548-4553
Author(s):  
N. T. Dung ◽  
N. T. Phuong
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Business Strategy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Short Term Forecasting

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

scholarly journals Air passengers forecasting for Australian airline based on hybrid rough set approach

2018 ◽  
Vol 14 (1) ◽  
pp. 5-18
Author(s):  
H. Kumar Sharma ◽  
K. Kumari ◽  
S. Kar
Keyword(s):  
Time Series ◽  
Set Theory ◽  
Rough Set ◽  
Hybrid Method ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Series Data ◽  
Long Term Forecasting ◽  
Des Model

Abstract Accurate and reliable air passenger demand is very important for policy-making and planning by tourism management as well as by airline authorities. Therefore, this article proposed a novel hybrid method based on rough set theory (RST) to construct decision rules for long-term forecasting of air passengers. Level (mean) and trend components are first estimated from the air passengers time series data using DES model in the formulation of the proposed hybrid method. Then the rough set theory is employed to combine the output of DES model and generated decision rules is used to forecasting air passengers. We compare the proposed approach with other time series models using a corrected classified accuracy (CCA) criterion. For the empirical analysis, yearly air transport passenger from 1992 to 2004 is used. Empirical results show that the proposed method is highly accurate with the higher corrected classified accuracy. Also, forecasting accuracy of the proposed method is better than the other time series approaches.

scholarly journals Earthquake forecasting using the rate-and-state friction model and a smoothing Kernel: application to Taiwan

2012 ◽  
Vol 12 (10) ◽  
pp. 3045-3057 ◽  
Author(s):  
C.-H. Chan ◽  
Y.-M. Wu ◽  
J.-P. Wang
Keyword(s):  
Friction Model ◽  
Earthquake Forecasting ◽  
Upper And Lower Bounds ◽  
Fault Interaction ◽  
Smoothing Kernel ◽  
Seismicity Rates ◽  
Rate And State Friction ◽  
Long Term Forecasting ◽  
Short Term Forecasting ◽  
The Stability

Abstract. In this work, two approaches were employed for estimating the spatiotemporal distribution of seismicity density in Taiwan. With the use of the rate-and-state friction model, a model for short-term forecasting according to the fault-interaction-based rate disturbance due to seismicity was considered. Another long-term forecasting model that involves a smoothing Kernel function is proposed. The application of the models to Taiwan led to good agreement between the model forecast and actual observations. Using an integration of the two approaches, the application was found to be capable of providing a seismicity forecast with a higher accuracy and reliability. To check the stability related to the regression the bandwidth function, the forecasted seismicity rates corresponding to the upper and lower bounds of the 95% confidence intervals are compared. The result shows that deviations within the bandwidth functions had an insignificant impact on forecasting reliability. Besides, insignificant differences in the forecasted rate change were obtained when Aσ was assumed to be between 0.1 and 0.4 bars for the application of the rate-and-state friction model. By considering the maximum Coulomb stress change among the seismogenic depth, the model presents a better forecasting ability than that using any single fixed target depth. The proposed methodology, with verified applicability for seismicity forecasts, could be useful for seismic hazard analyses.

Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo

2020 ◽  
pp. 097215092092331 ◽  
Author(s):  
Meena Madhavan ◽  
Mohammed Ali Sharafuddin ◽  
Pairach Piboonrungroj ◽  
Ching-Chiao Yang
Keyword(s):  
Airline Industry ◽  
Moving Average ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Air Cargo ◽  
Short Term ◽  
Commercial Aviation ◽  
Directorate General ◽  
Long Term Forecasting ◽  
Short Term Forecasting

This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate General of Civil Aviation (DGCA-India) website. The study assessed both ARIMA and BSTS models’ ability to incorporate uncertainty under dynamic settings. Findings inferred that, along with ARIMA, BSTS is also suitable for short-term forecasting of all four (international passenger, domestic passenger, international air cargo, and domestic air cargo) commercial aviation sectors. Recommendations and directions for further research in medium-term and long-term forecasting of the Indian airline industry were also summarized.

Structural Health Monitoring of Rotating Machines in Manufacturing Processes by Vibration Methods

2014 ◽  
Vol 1036 ◽  
pp. 642-647 ◽  
Author(s):  
Rafał Burdzik ◽  
Łukasz Konieczny ◽  
Piotr Folęga
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Vibration Signal ◽  
Technical Condition ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Manufacturing Processes ◽  
Diagnostic Systems ◽  
Structural Health ◽  
Run Up

The paper presents results of the active diagnostics experiments on influence of fatigue metal damage of the inner race of bearing and unbalance of rotating masses on vibration generated by the machine. Analysis of vibration related phenomena is a solution commonly applied in Structural Health Monitoring (SHM) systems. The application of vibroacoustics methods for technical condition monitoring has been developed in the past years in many systems of manufacturing processes. Vibroacoustic methods, based on the analysis of vibration or acoustic signals perceived as residual processes of non-invasive nature, is becoming more and more important in this respect. The scope of its application as well as the applicability of methods in numerous diagnostic systems also results from the capabilities of advanced methods of signal analysis and identification of numerous characteristics of technical condition. One of the most common operation damages are caused by rolling bearings wear. The scope of research contains tests on bearing damage and the unbalance of disc. The wear processes and unbalance are closely related to the vibration levels (arising from the mass loss of plastic deformation, and the fatigue damage). The research was conducted on special research test bench for vibration monitoring for rotating machinery. Structural health monitoring of machinery has to be conducted in different states and working conditions of the manufacturing system. Thus for simulating of different operating conditions the experiments have been conducted during run up of the machine which consist the transient states of working and during work on constant rotational speed of the power generate engine. For the identification of the symptoms of the machinery and equipments health monitoring the vibration signal have been analysed in time domain and frequency transformation as well. The performed signals are not stationary. Thus it is better to observe the signal simultaneously in time and frequency domains. For this purpose the spectrograms were determined. Spectrograms computes the short-time Fourier transform of a signal by default divided into segments. During the transformation the Hamming window and noverlap were used. For the comparison of the vibration of good and damage bearings signals registered for different frequencies have been presented in form of spectrograms and RMS distributions.

VIBRATION DIAGNOSTIC OF A FRICTION PROCESS IN SLIDE BEARINGS

Tribologia ◽  
2018 ◽  
Vol 278 (2) ◽  
pp. 73-80
Author(s):  
Dariusz LEPIARCZYK ◽  
Wacław GAWĘDZKI
Keyword(s):  
Measurement Data ◽  
Technical Condition ◽  
Diagnostic Methods ◽  
Diagnostic Systems ◽  
Vibration Displacement ◽  
Vibration Diagnostics ◽  
Slide Bearing ◽  
Bearing Sleeve ◽  
Vibration Parameters ◽  
Thermal Phenomena

An analysis of the condition of technical objects is carried out by diagnostic systems, the purpose of which is to detect irregularities in their operation and to prevent damages. In slide bearings, it applies to the diagnostic of friction and thermal phenomena of mating friction pairs. Among many methods of bearing diagnostics, special attention should be paid to vibration diagnostic methods based on measurements of relative vibration parameters or on absolute vibration (displacement, velocity, or acceleration of vibration). Methods of the vibration diagnostic of bearings rely on periodic or continuous measurements of relative vibration parameters of the bearing housing in relation to the rotor (in the case of slide bearings the measurements of the bearing sleeve in relation to the shaft neck) or absolute vibration parameters of the bearing housing (i.e. the sleeve in the case of slide bearing). The article presents a method of vibration diagnostics of friction phenomena that occur during the operation of slide bearings under various lubrication and load conditions. There are presented methods of analysis and the interpretation of measurement data obtained as a result of the conducted slide bearing tests on the laboratory stand. A method for assessing a technical condition of the slide bearing friction pairs is proposed.

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