Two-sided Cumulative Sum Research Articles

A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms

In order to reduce the negative impact of the large-scale grid connection of residential photovoltaic (PV) equipment on the distribution network, it is of great significance to realize the real-time accurate identification of the grid connection state and its switching of residential PV equipment from the distribution network side. This paper introduces a non-intrusive method for identifying residential PV systems using transient features, leveraging the temporal convolutional network (TCN) model with attention mechanisms. Firstly, the discrimination and redundancy of transient features for residential PV devices are measured using a feature selection method based on the semi-Fisher score and maximal information coefficient (MIC). This enables the construction of a subset of identification features that best characterize the PV devices. Subsequently, a sliding window two-sided cumulative sum (CUSUM) event detection algorithm, incorporating a time threshold, is proposed for the real-time capturing of PV state switching and grid connection behavioral events. This algorithm effectively filters out disturbances caused by the on/off cycles of low-power residential devices and captures the transient time windows of PV behaviors accurately. On this basis, a TCN model with attention mechanisms is proposed to match the discerned event features by assigning varying weights to different types of characteristics, thereby facilitating the precise recognition of a PV grid connection and state-switching events. Finally, the proposed method is validated on a custom-designed non-intrusive experimental platform, demonstrating its precision and real-time efficiency in practical applications.

Fault tolerant control of islanded AC microgrids under sensor and communication link faults using online recursive reduced-order estimation

Cyber-Physical Systems (CPSs) have become an inseparable part of the new industrial era referred to as Industry 4.0 the quintessential example of which are smart grids. With a cyber communication level integrated with the physical components, besides considerable merits came along some reliability and security concerns. To address these issues, there ought to be a supervisory level monitoring and control system to identify any faults within the system and balance out their potential damaging impacts. In this paper, we consider fault tolerant control (FTC) problem in one of the building blocks of smart grids i.e. microgrids. The FTC method is based on online recursive reduced-order model estimation and Klaman Filter (KF) residual generation. Then, two-sided cumulative sum (CUSUM) sequential change detection algorithm is employed for sensor fault or communication link failure detection. Lastly, a fault-tolerant logic is constructed to switch between actual data and the internal model for the control system.

Cumulative Sum Chart Modeled under the Presence of Outliers

Cumulative sum control charts that are based on the estimated control limits are extensively used in practice. Such control limits are often characterized by a Phase I estimation error. The presence of these errors can cause a change in the location and/or width of control limits resulting in a deprived performance of the control chart. In this study, we introduce a non-parametric Tukey’s outlier detection model in the design structure of a two-sided cumulative sum (CUSUM) chart with estimated parameters for process monitoring. Using Monte Carlo simulations, we studied the estimation effect on the performance of the CUSUM chart in terms of the average run length and the standard deviation of the run length. We found the new design structure is more stable in the presence of outliers and requires fewer amounts of Phase I observations to stabilize the run-length performance. Finally, a numerical example and practical application of the proposed scheme are demonstrated using a dataset from healthcare surveillance where received signal strength of individuals’ movement is the variable of interest. The implementation of classical CUSUM shows that a shift detection in Phase II that received signal strength data is indeed masked/delayed if there are outliers in Phase I data. On the contrary, the proposed chart omits the Phase I outliers and gives a timely signal in Phase II.

Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems

Pressure transients have been identified as one of the major contributing factors in many pipe failures in water distribution systems (WDSs). The behavior of these pressure transients is largely unknown and cannot be fully assessed by numerical simulation or modeling. This study investigates the behavior of pressure transients in WDSs as measured by high-frequency pressure sensors. A Time Series Data Mining (TSDM) approach is proposed to detect and cluster pressure transients to reveal recurrent and consistent patterns. The proposed technique, based on a modified two-sided cumulative sum (CUSUM) algorithm, is used to detect pressure transients. Dynamic Time Warping (DTW) is adopted to measure the similarity between the detected pressure transients, and k-means clustering algorithm is used to discover the characteristic patterns. Several performance scores are suggested to evaluate the quality of the clustering results, including sum of squared error, Silhouette index, and Calinski-Harabaz index. Results demonstrate that the proposed approach is able to reveal consistent and unique patterns across multiple sensing locations. The proposed approach provides a fast and efficient way to discover the hidden information in WDSs by analyzing high-frequency pressure signals from distributed sensors.

Two-Sided Cumulative Sum (cusum) Control Chart for Monitoring Shift in the Shape Parameter of the Pareto Distribution

Two-Sided Cumulative Sum (cusum) Control Chart for Monitoring Shift in the Shape Parameter of the Pareto Distribution

Model-based prognosis of fatigue crack growth under variable amplitude loading

In this paper, a model-based prognosis method using a particle filter that takes model uncertainty, measurement uncertainty and future loading uncertainty into account is proposed. First of all, a nonlinear analytical model of the degradation that depends on loading inputs is established. Then, a joint estimation of the unknown loading inputs and of the degradation state is performed with a particle filter. Moreover, a two-sided cumulative sum (CUSUM) algorithm is implemented to detect abrupt load variations and help the particle filter to adapt and learn new loading inputs values. With the combination of these two techniques, the prognosis module could be informed of the sudden crack length increase, and will correct the predicted remaining useful life. Real data from fatigue tests on fiber-reinforced metal matrix composite materials are used to demonstrate the efficiency of the proposed methodology for crack growth prognosis.

A Note on the ARL of Two-Sided Bernoulli-Based CUSUM Control Charts

In this article, we examine the average run length (ARL) properties of two types of two-sided Bernoulli cumulative sum (CUSUM) control charts. In particular, we give the conditions under which the ARL of the two-sided Bernoulli CUSUM chart can be determined exactly from the ARLs of the corresponding upper and lower Bernoulli CUSUM charts. This ARL relationship is established for both the standard industrial application and the risk-adjusted health-care monitoring application.

A Visualization Decision Support Tool for Multivariate SPC Diagnosis Using Marginal CUSUM Glyphs

ABSTRACT A marginal cumulative sum (CUSUM) glyph is proposed to visualize and decompose out-of-control signals over time. The proposed visualization tool, consisting of a two-sided CUSUM and star glyphs, is capable of indicating when and which variables contribute to the cause. Complementing traditional multivariate control charts after indicating that the process under monitoring is out of control, the proposed method provides a visualization tool with additional diagnostic information. In addition, the proposed tool is capable of handling responses with high dimensions. Extensive simulation results conducted for up to 20 dimensions provide a user guideline of how to implement the proposed methodology.

A Note on Two-Sided Cusums for a Normal Mean

A two-sided cumulative sum (cusum) scheme for a normal mean involves running two one-sided schemes, one to control upward and the other to control downward changes in the mean. We show that the two-sided scheme can be replaced by a single upward cusum scheme that has the same in-control average run length (ARL) and a smaller out-of-control ARL. The benefits of the new scheme are especially apparent at small mean shifts.

The random intrinsic fast initial response of two-sided CUSUM charts

Abstract This article analyses the performance of a one-sided cumulative sum (CUSUM) chart that is initialized using a random starting point following the natural or intrinsic probability distribution of the CUSUM statistic. By definition, this probability distribution remains stable as the chart is used. The probability that the chart starts at zero according to this intrinsic distribution is always smaller than one, which confers on the chart a fast initial response feature. The article provides a fast and accurate algorithm to compute the in-control and out-of-control average run lengths and run-length probability distributions for one-sided CUSUM charts initialized using this random intrinsic fast initial response (RIFIR) scheme. The algorithm also computes the intrinsic distribution of the CUSUM statistic and random samples extracted from this distribution. Most importantly, no matter how the chart was initialized, if no level shifts and no alarms have occurred before time τ > 0, the distribution of ...

Pipeline Break Detection Using Pressure Transient Monitoring

Sudden pipe breaks occur in water transmission pipelines and distribution mains. The consequences of these breaks can be very expensive because of the service interruption, the cost of repair, and damage to surrounding property and infrastructure. The costs associated with the pipeline breaks can be reduced by minimizing the break detection and location time. This paper presents a new continuous monitoring approach for detecting and locating breaks in pipelines. A sudden pipe break creates a negative pressure wave that travels in both directions away from the break point and is reflected at the pipeline boundaries. Using the pressure data measured at one location along the pipeline, the timing of the initial and reflected transient waves induced by the break determines the location of the break. The magnitude of the transient wave provides an estimate of the break size. The continuous monitoring technique uses a modified two-sided cumulative sum (CUSUM) algorithm to detect abrupt break-induced changes in the pressure data. The adaptive tuning of CUSUM parameters is implemented to detect breaks of differing sizes and opening times. The continuous monitoring technique is verified by using results from both laboratory and field experiments and shows potential for detecting and locating sudden breaks in real pipelines.

Multivariate Extensions to Cumulative Sum Control Charts

AbstractThere are several multivariate extensions to the venerable cumulative sum (CUSUM) chart. This work provides a comparison of the advantages and disadvantages of each, as well as performance evaluations and a description of their interrelationships. A new derivation is also provided. Extensive simulation results that include initial and steady‐state conditions are presented. Geometric descriptions are used, and names are proposed based on these geometric characteristics. As several of the key steps in the development of a multivariate extension to a CUSUM also appear in the construction of a two‐sided CUSUM, some approaches to a two‐sided CUSUM are also summarized. Copyright © 2004 John Wiley & Sons, Ltd.

A New Two-Sided Cumulative Sum Quality Control Scheme

A new two-sided cumulative sum quality control scheme is proposed. The new scheme was developed specifically to be generalized to a multivariate cumulative sum quality control scheme. The multivariate version will be examined in a subsequent paper; this article evaluates the univariate version. A comparison of the conventional two-sided cumulative sum scheme and the proposed scheme indicates that the new scheme has slightly better properties (ratio of on-aim to off-aim average run lengths) than the conventional scheme. Steady state average run lengths are discussed. The new scheme and the conventional two-sided cumulative sum scheme have equivalent steady state average run lengths. Methods for implementing the fast initial response feature for the new cumulative sum scheme are given. A comparison of average run lengths for the conventional and proposed schemes with fast initial response features is also favorable to the new scheme. A Markov chain approximation is used to calculate the average run lengths ...

A New Two-Sided Cumulative Sum Quality Control Scheme

A New Two-Sided Cumulative Sum Quality Control Scheme

On a unified approach to the analysis of two-sided cumulative sum control schemes with headstarts

The necessary and sufficient conditions for various modes of interactions of the upper and lower schemes with headstarts are derived. The expression for the Laplace transform of the run-length distribution (for both interacting and non-interacting schemes) is obtained and used to develop a method of analysis for general two-sided cumulative sum schemes with headstarts. The results are shown to be relevant in the case when the schemes are supplemented by Shewhart’s control limits.

On a unified approach to the analysis of two-sided cumulative sum control schemes with headstarts

The necessary and sufficient conditions for various modes of interactions of the upper and lower schemes with headstarts are derived. The expression for the Laplace transform of the run-length distribution (for both interacting and non-interacting schemes) is obtained and used to develop a method of analysis for general two-sided cumulative sum schemes with headstarts. The results are shown to be relevant in the case when the schemes are supplemented by Shewhart’s control limits.

On the Markov Chain Approach to the Two-Sided CUSUM Procedure

A Markov chain representation is given for the two-sided, possibly asymmetric, cumulative sum (CUSUM) procedure of Ewan and Kemp (1960) for integer-valued random variables. The approach taken by Brook and Evans (1972) for the one-sided CUSUM procedure to determine exact and approximate expressions for the run length distribution, its moments, and its percentage points is extended to the two-sided CUSUM procedure. The number of states included in the Markov chain is minimized in order to make the methods as efftcient as possible.

On the Markov Chain Approach to the Two-Sided CUSUM Procedure

A Markov chain representation is given for the two-sided, possibly asymmetric, cumulative sum (CUSUM) procedure of Ewan and Kemp (1960) for integer-valued random variables. The approach taken by Brook and Evans (1972) for the one-sided CUSUM procedure to determine exact and approximate expressions for the run length distribution, its moments, and its percentage points is extended to the two-sided CUSUM procedure. The number of states included in the Markov chain is minimized in order to make the methods as efftcient as possible.

A Note on Page's Two-Sided Cumulative Sum Procedure

A symmetric version of Page's (1954) two-sided cumulative sum procedure is discussed and a formula for the average run length given. Asymptotic normality of run length under suitable normalization is established.

A note on Page's two-sided cumulative sum procedure

SUMMARY A symmetric version of Page's (1954) two-sided cumulative sum procedure is discussed and a formula for the average run length given. Asymptotic normality of run length under suitable normalization is established.