Condition monitoring based on modified CUSUM and EWMA control charts

Abstract

Purpose The application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues. Design/methodology/approach Statistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts. Findings With data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures. Originality/value In this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

“…Through combining the advantage of the CUSUM-based method and the AA-based method, a CUSUM-AA-based method was developed to detect faults at earlier times. A condition monitoring method, based on modified CUSUM and exponentially weighted moving average (EWMA) control charts, was proposed for detecting system or equipment failure trend [19]. The method was validated using an electro pump equipment.…”

“…Whenever the cumulations reach either a high or low limit, an out-of-control signal is triggered. Second, all methods reported in [13][14][15][16][17][18][19][20][21] used this type of CUSUM control chart as a supporting or auxiliary tool to improve their condition monitoring and/or fault diagnosis. Specifically, in these previous studies the CUSUM control chart technique was combined with other techniques, such as ANNs [13,14], PCA [15,16,20], EEMD-based PCA [17], AA-based PCA [18], EWMA [19], and RF [21], for fault detection and diagnosis.…”

Condition-Based Maintenance Optimization for Motorized Spindles Integrating Proportional Hazard Model with SPC Charts

“…Integrated models combining the X control chart with PM actions were proposed in [5,6] in order to protect the equipment from quality decline. Lampreia et al [7] modified CUSUM and exponentially weighted moving average (EWMA) control charts for online vibration monitoring, thus reducing false alarms and planning early interventions. e similar modelling idea was also adopted for the economic design of maintenance plans with the Shewhart control chart, chi-square control chart, etc., as discussed in [8][9][10][11][12].…”

Monitoring process variation using modified EWMA

“…Latterly, Schmid proposed modified EWMA control chart for monitoring the time series data. Other work on the development of EWMA control charts based on modified EWMA statistics includes Aslam et al, Khan et al, Herdiani et al, Zhang et al, Lampereia et al, etc. All these articles concluded that the modified EWMA control chart is more capable of detecting early shift in process parameter under study as compared with the traditional EWMA control chart.…”