Rebooting data-driven soft-sensors in process industries: A review of kernel methods

Simultaneous monitoring of magnitude and time-between-events data with a Max-EWMA control chart

Monitoring of time between events with a double generally weighted moving average control chart

Monitoring multivariate and high-dimensional data streams is often an essential requirement for quality management in manufacturing and service sectors in the Industry 4.0 era. Identifying a suitab...

Nonparametric Phase-II control charts for monitoring high-dimensional processes with unknown parameters

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results. 

Artificial Intelligence Enhanced Two-Stage Hybrid Fault Prognosis Methodology of PMSM

Integrated design of monitoring, analysis and maintenance for filamentous sludge bulking in wastewater treatment

Distribution-free EWMA schemes for simultaneous monitoring of time between events and event magnitude

Two CUSUM schemes for simultaneous monitoring of parameters of a shifted exponential time to events

In this paper, control charts for monitoring the exponential type-II censoring samples are investigated. Such data are very common in many practical inspection scenarios in reliability context when items are replaced in groups after a period of time. The average time to signal, which involves both the number and the time of samples inspected until a signal occurs, is a good criterion to evaluate the performance of control charts. We propose an average time to signal-unbiased control chart with known parameter and compare the proposed method with the traditional ones. The results indicate the proposed control chart is more sensitive to system deterioration. Then the effects of parameter estimation on the proposed control charts are evaluated. Because the control limits with estimated parameters result in more false alarms, an adjusted control chart with estimated parameters is proposed and the self-starting control chart based on a sequential sampling scheme is adopted to solve the phase I problem. Finally, two examples are given to illustrate the implementation of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.

A Study of Control Chart for Monitoring Exponentially Distributed Characteristics Based On Type-II Censored Samples

Quality data fraud not only destroys the trust between suppliers and customers but also misleads the decision-making when choosing suppliers. Thus, it is preferred to use the quality data measured ...

Shuo Huang

Papers

Integrated design of monitoring, analysis and maintenance for filamentous sludge bulking in wastewater treatment

Distribution-free EWMA schemes for simultaneous monitoring of time between events and event magnitude

Two CUSUM schemes for simultaneous monitoring of parameters of a shifted exponential time to events

A Study of Control Chart for Monitoring Exponentially Distributed Characteristics Based On Type-II Censored Samples

Process capability analysis for manufacturing processes based on the truncated data from supplier products