Combination of statistical process control (SPC) methods and classification strategies for situation assessment of batch processes

Postprint (published version

Temperature robust PCA based stress monitoring approach

In this paper, a guided wave temperature robust PCA-based stress monitoring methodology is proposed. It is based on the analysis of the longitudinal guided wave propagating along the path under stress. Slight changes in the wave are detected by means of PCA via statistical T2 and Q indices. Experimental and numerical simulations of the guided wave propagating in material under different temperatures have shown significant variations in the amplitude and the velocity of the wave. This condition can jeopardize the discrimination of the different stress scenarios detected by the PCA indices. Thus, it is proposed a methodology based on an extended knowledge base, composed by a PCA statistical model for different discrete temperatures to produce a robust classification of stress states under variable environmental conditions. Experimental results have shown a good agreement between the predicted scenarios and the real onesPostprint (author's final draft

Flow estimation in a steel pipe using guided waves

In this investigation, a flow rate estimation guided wave based scheme in pipes is proposed. The effect of the fluid over the propagation of longitudinal waves has been experimentally studied by using several laminar flows of water transported by a steel pipe. Results have shown a decrease of the guided wave pattern repeatability and the signal energy as the flow rate increase as a result of the energy leakage from the pipe to the fluid. A Matlab® script is used to excite the PZT actuator via picoscope 2208 of Picotech®, the captured signal is acquired also by the picoscope and the data is processed in Matlab. The test bench utilized is composed by a 1” sch 40 A-106 pipe, a needle valve and a centrifugal pump provides the flow energy. A couple of PZTs are used in a picth-catch configuration to produce and capture the longitudinal waves along the cross section of the pipe.Postprint (author's final draft

Combination of multivariate statistical process control and classification tool for situation assessment applied to a sequencing batch reacto wastewater treatment

A combination of Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm has been developed to be applied in a Waste Water Treatment Plant. Two extensions of the Principal Component Analysis have been used as MSPC method to diagnose the process and Fuzzy Technique used to classify situation assessment of the process. The goal is to perform situation assessment and classify the process with simple groups that describe the batch contributions and helps to fix the limits used to determine abnormal situations.Postprint (published version

Multiway principal component analysis and case base reasoning methodology for abnormal situation detection in a nutrient removing SBR

Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) approaches are applied in a biological nutrient removal process. The goal is monitoring of normal and abnormal operation conditions in this process. MPCA is used as a compression tool where with few variables the process can be described, as well as, to detect batches with abnormal conditions. However, some abnormal conditions (alarms) are omitted or, otherwise some false alarms are produced. Then, CBR is proposed. It assumes that similar problems should have similar solutions. In biological process, these problems generally are operational situations under normal or abnormal situations: low ORP, high pH, sensors fault, among others. These symptoms can be stored in a Case Base (CB) in order to diagnose future situations. Several proof are made in order to find the better methodology.Postprint (author’s final draft

Multivariable statistical process control to situation assessment of a sequencing batch reactor

In this work, a combination between Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm is developed to application in Waste Water Treatment Plant. Multiway Principal Component Analysis is used as MSPC method. The goal is to create a model that describes the batch direction and helps to fix the limits used to determine abnormal situations. Then, an automatic classification algorithm is used to situation assessment of the process.Postprint (published version

Evaluation of piezodiagnostics approach for leaks detection in a pipe loop

Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.Peer ReviewedPostprint (author's final draft

PCA based stress monitoring of cylindrical specimens using PZTs and guidedwaves

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.Peer ReviewedPostprint (published version

Analysis of the torsional mode T(0,1) propagating in a bending pipe

In operation, some structural members are subjects to bending conditions. Velocity variations of guided waves propagating in a stressed specimen are expected due to the acoustoelasticity effect. In this work, a numerical study of the fundamental torsional mode is performed in a hollow cylinder subjects to bending stress. Bending produces different stresses along the horizontal axis of the pipe and through the specimen thickness. Therefore, stress magnitudes are dependent on the longitudinal axis distance and the radius. Acoustoelasticity predicts change in bulk velocities when the propagation is in a stressed waveguide. Thus, the estimation of the phase velocity of a guided wave in a bowing specimen is a complex task. The objective of this study is to assess changes in the wavepacket dynamics due to stress variations in a bending environment. In the numerical analysis by using Finite Element Modeling (FEM), the stress gradient is imposed on the wave propagation analysis by importing the results of the quasi-static simulation (bending) to the explicit solution FEM scheme (Wave propagation). Several bending’s stresses behavior are simulated (variable and constant along the waveguide) and implemented to establish a relationship between the bending stress and the change in the phase magnitude velocity. Variations in velocity are determined in the time domain by comparing the current signals with the wavepacket without bending. The importance of this study lies on the verification of the effects in the wave field (e.g. velocity variations) caused by the bending and determine if they are enough to be used to track stresses variations in the waveguide in an SHM system by using a pitch-catch configuration.Postprint (published version

Implementation of a piezo-diagnostics approach for damage detection based on PCA in a linux-based embedded platform

The implementation of damage-detection methods for continuously assessing structural integrity entails systems with attractive features such as storage capabilities, memory capacity, computational complexity and time-consuming processing. In this sense, embedded hardware platforms are a promising technology for developing integrated solutions in Structural Health Monitoring. In this paper, design, test, and specifications for a standalone inspection prototype are presented, which take advantage of piezo-diagnostics principle, statistical processing via Principal Component Analysis (PCA) and embedded systems. The equipment corresponds to a piezoelectric active system with the capability to detect defects in structures, by using a PCA-based algorithm embedded in the Odroid-U3 ARM Linux platform. The operation of the equipment consists of applying, at one side of the structure, wide guided waves by means of piezoelectric devices operated in actuation mode and to record the wave response in another side of the structure by using the same kind of piezoelectric devices operated in sensor mode. Based on the nominal response of the guide wave (no damages), represented by means of a PCA statistical model, the system can detect damages between the actuated/sensed points through squared prediction error (Q-statistical index). The system performance was evaluated in a pipe test bench where two kinds of damages were studied: first, a mass is added to the pipe surface, and then leaks are provoked to the pipe structure by means of a drill tool. The experiments were conducted on two lab structures: (i) a meter carbon-steel pipe section and (ii) a pipe loop structure. The wave response was recorded between the instrumented points for two conditions: (i) The pipe in nominal conditions, where several repetitions will be applied to build the nominal statistical model and (ii) when damage is caused to the pipe (mass adding or leak). Damage conditions were graphically recognized through the Q-statistic chart. Thus, the feasibility to implement an automated real-time diagnostic system is demonstrated with minimum processing resources and hardware flexibility.Peer ReviewedPostprint (published version