Monitoring Digital Technologies in Hydraulic Systems Using CUSUM Control Charts

2019 ◽  
Author(s):  
Farid Breidi ◽  
Abdallah Chehade ◽  
John Lumkes
Keyword(s):  
Time Series ◽  
Real Time ◽  
Condition Monitoring ◽  
Control Charts ◽  
High Speed ◽  
Fluid Power ◽  
Time Shift ◽  
Statistical Process ◽  
Pressure Time ◽  
Cusum Control Charts

Abstract Digital fluid power is a growing field which utilizes electronics and advanced controls to improve efficiencies, energy savings, and productivity in fluid power systems. Often relying on on/off high-speed switching techniques, digital hydraulics relies heavily on the performance of valves, where an error in the valve performance could lead to a major drop in the efficiency and performance of the entire system. Specifically, digital pump/motors are sensitive to valve delay and transition timing which negatively impacts their performance and condition with time. One approach to assessing the performance and efficiency of digital pump/motors is via monitoring its inlet (low) and outlet (high) pressure time-series. Real-time condition monitoring also supports preventive maintenance and provides a better understanding of the dynamics of pump/motors. For condition monitoring, Statistical Process Control (SPC) charts are often designed to detect shift changes in time-series. This paper proposes to construct two cumulative sum (CUSUM) control charts for fast real-time shift detection in the high and low pressure time-series of digital pump/motors. The proposed method will be able to actively detect common misbehaviors in the valves utilized in the digital pump/motor. The model have been successfully tested on a three-piston inline digital pump/motor, but this monitoring technique can be modified and implemented on other digital technology classes where valve performance is key in the success of the system.

TQWT-assisted statistical process control method for condition monitoring and fault diagnosis of bearings in high-speed rail

2020 ◽  
pp. 1748006X2095832 ◽  
Author(s):  
Wei Fan ◽  
Hongtao Xue ◽  
Cai Yi ◽  
Zhenying Xu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
High Speed ◽  
Control Method ◽  
Wavelet Coefficients ◽  
Frequency Method ◽  
Statistical Process ◽  
High Speed Rail ◽  
Distribution Method ◽  
Time Frequency

Condition monitoring and fault diagnosis of bearings in high-speed rail have attracted considerable attention in recent years, however, it’s still a hard work due to harsh environments with high speeds and high loads. A statistical condition monitoring and fault diagnosis method based on tunable Q-factor wavelet transform (TQWT) is developed in this study. The core idea of this method is that the TQWT can extract oscillatory behaviors of bearing faults. The vibration data under the normal condition are first decomposed by the TQWT into different wavelet coefficients. Two health indicators are then formulated by the dominant wavelet coefficients and the remaining coefficients for condition monitoring. The upper control limits are established using the one-sided confidence limit of the indicators by using the non-parametric bootstrap scheme. The Shewhart control charts on multiscale wavelet coefficients are constructed for fault diagnosis. We demonstrate the effectiveness of the proposed method by monitoring and diagnosing single and multiple railway axle bearing defects. Furthermore, the comparison studies show that the proposed method outperforms a traditional time-frequency method, the Wigner-Ville distribution method.

Time Series Control Charts in the Presence of Model Uncertainty

2002 ◽  
Vol 124 (4) ◽  
pp. 891-898 ◽  
Author(s):  
Daniel W. Apley
Keyword(s):  
Time Series ◽  
Model Uncertainty ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Process Data ◽  
Estimation Errors ◽  
Statistical Process ◽  
Modeling Errors ◽  
Autocorrelated Processes

Time series control charts are popular methods for statistical process control of autocorrelated processes. In order to implement these methods, however, a time series model of the process is required. Since time series models must always be estimated from process data, model estimation errors are unavoidable. In the presence of modeling errors, time series control charts that are designed under the assumption of a perfect model may have an actual in-control average run length that is substantially shorter than desired. This paper presents a method for incorporating model uncertainty information into the design of time series control charts to provide a level of robustness with respect to modeling errors. The focus is on exponentially weighted moving average charts and Shewhart individual charts applied to the time series residuals.

scholarly journals USAGE OF CONTROL CHARTS FOR TIME SERIES ANALYSIS IN FINANCIAL MANAGEMENT

2014 ◽  
Vol 16 (1) ◽  
pp. 138-158 ◽  
Author(s):  
Martin Kovářík ◽  
Libor Sarga ◽  
Petr Klímek
Keyword(s):  
Time Series ◽  
Process Control ◽  
Statistical Process Control ◽  
Financial Management ◽  
Financial Stability ◽  
Control Charts ◽  
Practical Interest ◽  
Cash Flows ◽  
Statistical Process ◽  
Financial Flows

We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951–1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest.

scholarly journals Control Limits for an Adaptive Self-Starting Distribution-Free CUSUM Based on Sequential Ranks

Technologies ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 71
Author(s):  
Michael Lang
Keyword(s):  
Control Charts ◽  
Simulation Experiments ◽  
Statistical Process ◽  
Distribution Free ◽  
Extensive Simulation ◽  
Detection Delay ◽  
Calibration Algorithm ◽  
Cusum Control Charts ◽  
Control Limits ◽  
Large Representative

Since their introduction in 1954, cumulative sum (CUSUM) control charts have seen a widespread use beyond the conventional realm of statistical process control (SPC). While off-the-shelf implementations aimed at practitioners are available, their successful use is often hampered by inherent limitations which make them not easily reconcilable with real-world scenarios. Challenges commonly arise regarding a lack of robustness due to underlying parametric assumptions or requiring the availability of large representative training datasets. We evaluate an adaptive distribution-free CUSUM based on sequential ranks which is self-starting and provide detailed pseudo-code of a simple, yet effective calibration algorithm. The main contribution of this paper is in providing a set of ready-to-use tables of control limits suitable to a wide variety of applications where a departure from the underlying sampling distribution to a stochastically larger distribution is of interest. Performance of the proposed tabularized control limits is assessed and compared to competing approaches through extensive simulation experiments. The proposed control limits are shown to yield significantly increased agility (reduced detection delay) while maintaining good overall robustness.

Research on the Technology of Intelligent Switch Components Device

2012 ◽  
Vol 241-244 ◽  
pp. 1936-1941
Author(s):  
Jia Jia Hou ◽  
Yue Ming Cai
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
High Speed ◽  
Low Voltage ◽  
Hardware Platform ◽  
Solid Foundation ◽  
Device Research

Because of many present problems of the intelligent switch in the intelligent substation, it is necessary to study on intelligent switch components device. The hardware platform of the components device was based on high-speed low-voltage differential bus (BLVDS). By improving real-time characteristics, real-time communication was available for the substation process bus. It is also described that the solution to online condition monitoring of the components device. Research on these aspects laid a solid foundation for the intelligent switch components device.

scholarly journals Detection of changes in time-series of indicators using CUSUM control charts

2009 ◽  
Vol 22 (2) ◽  
pp. 187-192 ◽  
Author(s):  
Benoit Mesnil ◽  
Pierre Petitgas
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Cusum Control Charts

scholarly journals DESCRIPTION AND IDENTIFICATION OF TEMPORAL REGULARITIES FOR FUZZY TIME SERIES WITH APPLICATION OF HYBRID OLS-PATTERNS

2018 ◽  
Vol 45 (2) ◽  
pp. 104-113
Author(s):  
D. V. Dultsev ◽  
L. I. Suchkova
Keyword(s):  
Time Series ◽  
Knowledge Base ◽  
Real Time ◽  
Fuzzy Time Series ◽  
Time Shift ◽  
Partial Data ◽  
Linear List ◽  
Fuzzy Linguistic Variable ◽  
Observable Data ◽  
Scientific Results

ObjectivesThe aim of the research is to develop the principle of storing data templates to take their temporal natureinto account, making it possible to reduce decision-making times.In order to describe and identify temporal patterns in fuzzy time series behaviour in real time, the task was set to develop a hybrid data structure that allows for a consideration of sequences of fuzzy values formed from clear observable data as well as a determination of the length of these sequences and possible uneven time intervals between the observations.MethodsThe article discussesan approach to formalising the description of temporal cause-effect relationships between events occurring at the object location as well as that of its environment, based on a set of singly-connected lists of triplets. Each triplet contains a fuzzy linguistic variable, the duration of its observation and the permitted interval of observation of insignificant data.ResultsAn algorithm for detecting knowledge base patterns in real time was developed, taking into account the possibility of a time shift in observing long sequences of identical values of the observed value. The possibility of partial data overlapping corresponding to triplets of different patterns is taken into account. The proposed hybrid pattern makes it possible to accelerate the detection of temporal regularities in the data.ConclusionScientific results are presented by the developed structure for storing information on temporal regularities in data, based on a singly linked linear list, as well as an algorithm for finding regularities in observational data using a set of OLS-patterns. The advantage of this structure and algorithm in comparison with the known ways of storing and analysing temporal data is a reduction in the amount of memory necessary for storing templates in the knowledge base, as well as the possibility of applying OLS patterns for decisionmaking purposes.

Deep Learning for Downhole Data Prediction: A Cost-Effective Data Telemetry Through Data Analytics

2021 ◽  
Author(s):  
Bharat Thakur ◽  
Robello Samuel
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Real Time ◽  
High Speed ◽  
Cost Effective ◽  
Time Series Forecasting ◽  
Data Prediction ◽  
Surface Data ◽  
Median Error

Abstract Accurate real-time downhole data collection provides a better understanding of downhole dynamics and formation characteristics, which can improve wellbore placement and increase drilling efficiency by improving the rate of penetration (ROP) and reducing downtime caused by tool failure. High-speed telemetry through wired drill string has enabled real-time data acquisition, but there are significant additional costs associated with the technology. Data-driven techniques using recursive neural networks (RNN) have proven very efficient and accurate in time-series forecasting problems. In this study, we propose deep learning as a cost-effective method to predict downhole data using surface data. Downhole drilling data is a function of surface drilling parameters and downhole conditions. The downhole data acquired using relatively inexpensive methods usually have a considerable lag time depending on the signal travel length. So, the first step in the proposed method is syncing the downhole data and surface data. After the data are synced, they are then fed into an RNN-based long-term short memory (LSTM) network, which learns the relationship between the surface parameters and downhole data. LSTM networks can learn long-term relationships in the data, thus making them ideal for time-series forecasting applications. The trained model is then used to make predictions for downhole data using the given surface data. The median error for the prediction of downhole data using surface data was as low as 3% in this study. The study suggests that the developed model can accurately predict downhole data in real-time. The model is also very robust to the amount of noise or outliers present in the data and can predict downhole conditions 50–60 ft ahead with reasonable accuracy. It was observed that the prediction accuracy varied from well to well and drilling depths. The results demonstrate how deep learning can be cost-effectively employed for downhole data prediction. This paper presents a novel method for using surface data to predict downhole data by employing deep learning. The method can be deployed in real-time to aid in wellbore placement and improve drilling performance.

scholarly journals Condition Monitoring of Industrial Gas Turbine Critical Operating Parameters Using Statistical Process Control Charts

2015 ◽  
Vol 773-774 ◽  
pp. 204-209
Author(s):  
Harindharan Jeyabalan ◽  
Lim Meng Hee ◽  
Mohd Salman Leong
Keyword(s):  
Process Control ◽  
Gas Turbine ◽  
Statistical Process Control ◽  
Condition Monitoring ◽  
Control Charts ◽  
Operating Parameters ◽  
Statistical Process ◽  
Process Control Charts ◽  
Statistical Process Control Charts ◽  
Industrial Gas Turbine

This paper presents condition monitoring of industrial gas turbine by monitoring its critical operating parameters using statistical process control. This will consequently enables the detection of any degradation of gas turbine operating parameters and thus to better prepare for any forward actions that required. Basically performance of gas turbine and its critical operating parameters degrades over time. These parameters however degrades and eventually reach the OEM recomended limits without even triggereing any earlier alerts. Therefore, corrective maintenance actions are required to bring the parameters back to an acceptable operating condition which causing downtime in operation and accounts for large maintenance together with operating costs. Hence by identifying any degradation and deviation in gas turbine parameters in advance before it reaches its OEM limit will help to improve maintenance scheduling and practices and thus enhanced the reliability of the machine. It also able to identify false alarms and shutdowns which can cause unnecessary maintenance and non profitable stops. SFC method is also found to be able to estimate the progression of component/ performance degradation and thereby generating a continuously updated prediction of the remaining useful life of machine components. SPC based machine condition monitoring uses statistical process control charts such as individual and moving range methods to create the operating threshold of the machine. These thresholds were showed to be capable to determine and identify performance degradation in advance or earlier before it reaches the OEM limits for each individual parameters.

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