Magda Ruiz

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.

INFLUENCE OF SCALING AND UNFOLDING IN PCA BASED MONITORING OF NUTRIENT REMOVING BATCH PROCESS

Abstract The data set of batch biological and biotechnological processes can be organized in a three-way data matrix. In this paper the usefulness of different PCA approaches for monitoring is analyzed. Different ways of unfolding and scaling of data have been applied to a pilot-scale SBR data. PCA is used to reduce the dimensionality and to remove the non-linearity dynamic of the data. Moreover, a new method to select the number of principal components is proposed. Loadings graphics are used to determinate the predominant variables for each one. The results show that whatever model can be applied depending on the goal of the monitoring, however the models implicate possible false alarms or faults omission.

Monitoring a Sequencing Batch Reactor for the Treatment of Wastewater by a Combination of Multivariate Statistical Process Control and a Classification Technique

A combination of Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm is applied to monitor a Waste Water Treatment Plant (WWTP). The goal of this work is to evaluate the capabilities of these techniques for assessing the actual state of a WWTP. The research was performed in a pilot WWTP operating with a Sequencing Batch Reactor (SBR). The results obtained refer to the dependence of process behavior with environmental conditions and the identification of specific abnormal operating conditions. It turned out that the combination of tolls yields better classifications compared with those obtained by using methods based on Partial Least Squares.

PCA Based Stress Monitoring of Cylindrical Specimens Using PZTs and Guided Waves

Since mechanical stress in structures affects issues such as strength, expected operational life and dimensional stability, a continuous stress monitoring scheme is necessary for a complete integrity assessment. Consequently, this paper proposes a stress monitoring scheme for cylindrical specimens, which are widely used in structures such as pipelines, wind turbines or bridges. The approach consists of tracking guided wave variations due to load changes, by comparing wave statistical patterns via Principal Component Analysis (PCA). Each load scenario is projected to the PCA space by means of a baseline model and represented using the Q-statistical indices. Experimental validation of the proposed methodology is conducted on two specimens: (i) a 12.7 mm (1/2″) diameter, 0.4 m length, AISI 1020 steel rod, and (ii) a 25.4 mm (1″) diameter, 6m length, schedule 40, A-106, hollow cylinder. Specimen 1 was subjected to axial loads, meanwhile specimen 2 to flexion. In both cases, simultaneous longitudinal and flexural guided waves were generated via piezoelectric devices (PZTs) in a pitch-catch configuration. Experimental results show the feasibility of the approach and its potential use as in-situ continuous stress monitoring application.

In-line Inspection of Pipelines by Using a Smart Pig (ITION) and Multivariate Statistical Analysis

The ferrous pipe structures of oil and gas production and, the transmission pipelines are, in majority, buried. Nowadays, phenomena like corrosion, mechanical stress, soil erosion, worker mistakes and damages caused by third parts have generated several problems over pipelines. Thus, major investment on integrity programs with In-Line Inspection Tools has been improved in order to examine the pipelines and avoid environmental, financial and social disasters. Recently in Colombia, the Research Institute of Corrosion - CIC (Corporacion para la Investigacion de la Corrosion) runs their own smart pig ILI tool in pipelines. The inspection technology is based on inertial and operational trends, ITION (Inertial Technology Inspection and Operational Trends). Up to date, the technology has been tested several times inside of pipelines providing valuable information along of thousand kilometres. These records contain a huge amount of data that sometimes is difficult or impossible to understand by themselves. A univariate statistical analysis can be used to determine the thresholds for each observation variable. However, it does not analyse the correlated information between them. In this way, the main contribution of this work is the development of a methodology based on Principal Component Analysis (PCA) to monitor the structure by using the whole available variables gathered by ITION. doi: 10.12783/SHM2015/291

Influence of Scaling and Unfolding in PCA Based Monitoring of Nutrient Removing Batch Process

: nThe data set of batch biological and biotechnological processes can be organized in a three-way data matrix. In this paper the usefulness of different PCA approaches for monitoring is analyzed. Different ways of unfolding and scaling of data have been applied to a pilot-scale SBR data. PCA is used to reduce the dimensionality and to remove the non-linearity dynamic of the data. Moreover, a new method to select the number of principal components is proposed. Loadings graphics are used to determinate the predominant variables for each one. The results show that whatever model can be applied depending on the goal of the monitoring, however the models implicate possible false alarms or faults omission.

Multiway partial least square (MPLS) to estimate impact localization in structures

This paper presents results from the application of Multiway Partial Least Square n(MPLS) as a regressor tool in order to estimate the localization of impacts in an aircraft structure. MPLS is a technique that maximizes the covariance between the predictor matrix X and the predicted matrix Y for each component of the space. The structure can be considered as a small scale version of part of a wing aircraft. 574 experiments were performed impacting the wing over its surface and receiving vibration signals from nine sensors. Experiments are divided in four groups depending on their localization and probability of occurrence. A PLS model is build using three of these groups and tested using the remaining group. Results are presented, discussed and compared with results of other methods.

Statistical modelling for fault diagnosis of sensors of the Ariane's engine

Abstract Variance of reconstructed error (VRE) is used in this work to select the set of sensors to be included in a PCA model for sensor fault diagnosis of the engine of the european rocket Ariane. Only fault sensors are considered in the study. The same principle is applied to analyse the number of principal component to be included in the model based on the reconstructability of them. The trade off between optimality and dimensionality is discussed in the paper.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.

Support Stiffness Monitoring of Cylindrical Structures Using Magnetostrictive Transducers and the Torsional Mode T(0,1)

In this paper, a support stiffness monitoring scheme based on torsional guided waves for detecting loss of rigidity in a support of cylindrical structures is presented. Poor support performance in cylindrical specimens such as a pipeline setup located in a sloping terrain may produce a risky operation condition in terms of the installation integrity and the possibility of human casualties. The effects of changing the contact forces between support and the waveguide have been investigated by considering variations in the load between them. Fundamental torsional T(0,1) mode is produced and launched by a magnetostrictive collar in a pitch-catch configuration to study the support effect in the wavepacket propagation. Several scenarios are studied by emulating an abnormal condition in the support of a dedicated test bench. Numerical results revealed T(0,1) ultrasonic energy leakage in the form of SH0 bulk waves when a mechanical coupling between the cylindrical waveguide and support is yielded. Experimental results showed that the rate of ultrasonic energy leakage depends on the magnitude of the reaction forces between pipe and support; so different levels of attenuation of T(0,1) mode will be produced with different mechanical contact conditions. Thus, it is possible to relate a measured attenuation to variations in the supports condition. Results of each scenarios are presented and discussed demonstrating the feasibility and potential of tracking of the amplitude of the T(0,1) as an indicator of abnormal conditions in simple supports.