Abstract-This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models.In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state.Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method. ATA acquisition systems that deal with large quantities of input variables and have higher sampling frequencies result in high bandwidth communication and place a heavy computational load on the higher tier data processing and information systems within their hierarchy. The focus of researchers and practitioners in this area has been to minimize this computational overhead by eliminating input variables that have the least impact on the system, this so called sensitivity analysis is discussed in [1]- [6]. Sensitivity analysis techniques help system analysts to focus on the most valuable information, information that most significantly impacts on system behaviour. Sensitivit...

ABSTRACT

The purpose of this paper is in two folds; the first is to undertake a thorough appraisal of the existing input variable selection (IVS) methods in the context of time-critical and resource-limited dimensionality reduction problems. The second is to demonstrate further improvements and the application of a recently proposed time-critical sensitivity analysis method called EventTracker in industry. Producing accurate knowledge about the state of a system (effect) in real-time under computational and data acquisition (cause) constraints is a major challenge. Especially if the knowledge required is critical in operations that the safety of operators and/or integrity of equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result into a specific state of the system is the focus of this research challenge. The objective is to identify which set of input data/signal has significant impact on the set of system state information (i.e. output). Through this cause-effect analysis process, the proposed technique filters unsolicited data that may clog up communication and computational capabilities of a standard Supervisory Control and Data Acquisition System. The outcome of this research project is a series of issues adhered to and suggesting the difficulty of finding an established method suitable for time-critical variable selection applications. However, supported by a geological drilling monitoring application, the authors are able to substantiate the aptness of the EventTracker Sensitivity Analysis method in high volume and time critical dimensionality reduction. In order to prove the advantages gained in performance and computational efficiency by adopting the proposed sensitivity analysis method, a general comparison and evaluation of other established input variable selection techniques is conducted.

Abstract-This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments' Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant's

An enhanced technique using image processing has been developed for automated ultrasonic 10 inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to 11 ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality 12 features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan 13 images. This work presents results performed on two composite samples with simulated delamination 14 defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective 15 and healthy regions in the produced C-Scan image. In this test campaign, different filtering and 16 thresholding algorithms are evaluated and compared in term of their effectiveness on defect 17 identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and 18 depth respectively. The results demonstrates the applicability of the proposed technique for accurate 19 defect localization and characterization of composite materials.