Applied Sensor Fault Detection, Identification and Data Reconstruction

Sensor fault detection and identification (SFD/I) has attracted considerable attention in military applications, especially when safety- or mission-critical issues are of paramount importance. Here, two readily implementable approaches for SFD/I are proposed through hierarchical clustering and self-organizing map neural networks. The proposed methodologies are capable of detecting sensor faults from a large group of sensors measuring different physical quantities and achieve SFD/I in a single stage. Furthermore, it is possible to reconstruct the measurements expected from the faulted sensor and thereby facilitate improved unit availability. The efficacy of the proposed approaches is demonstrated through the use of measurements from experimental trials on a gas turbine. Ultimately, the underlying principles are readily transferable to other complex industrial and military systems

Fault detection and diagnosis based on extensions of PCA

The paper presents two approaches for fault detection and discrimination based on principal component analysis (PCA). The first approach proposes the concept of y-indices through a transposed formulation of the data matrices utilized in traditional PCA. Residual errors (REs) and faulty sensor identification indices (FSIIs) are introduced in the second approach, where REs are generated from the residual sub-space of PCA, and FSIIs are introduced to classify sensor- or component-faults. Through field data from gas turbines during commissioning, it is shown that in-operation sensor faults can be detected, and sensor- and component-faults can be discriminated through the proposed methods. The techniques are generic, and will find use in many military systems with complex, safety critical control and sensor arrangements

Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems

The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine

Applied sensor fault detection and identification during steady-state and transient system operation

The paper presents two readily implementable methods for sensor fault detection and identification (SFD/I) for complex systems. Specifically, principal component analysis (PCA) and self-organizing map neural network (SOMNN) based algorithms are demonstrated for use on industrial gas turbine (IGT) systems. Two operational regimes are considered viz. steady-state operation and operation during transient conditions. For steady-state operation, PCA based squared prediction error (SPE) is used for SFD, and through the use of contribution plots, SFI. For SFD/I under operational conditions with transients, a proposed ‘y-index’ is introduced based on PCA with transposed input matrix that provides information on anomalies in the sensor domain (rather than in the time domain as with the traditional PCA approach). Moreover, using a SOMNN approach, during steady-state operation the estimation error (EE) is used for SFD and EE contribution plots for SFI. Additionally, during transient operation, SOMNN classification maps (CMs) are used through comparisons with ‘fingerprints’ taken during normal operation. Validation of the approaches is demonstrated through experimental trial data taken during the commissioning of IGTs. Although the attributes of the techniques are focused on a particular industrial sector in this case, ultimately their use is expected to be much more widely applicable to other fields and systems

Self-organising symbolic aggregate approximation for real-time fault detection and diagnosis in transient dynamic systems

The development of accurate fault detection and diagnosis (FDD) techniques are an important aspect of monitoring system health, whether it be an industrial machine or human system. In FDD systems where real-time or mobile monitoring is required there is a need to minimise computational overhead whilst maintaining detection and diagnosis accuracy. Symbolic Aggregate Approximation (SAX) is one such method, whereby reduced representations of signals are used to create symbolic representations for similarity search. Data reduction is achieved through application of the Piecewise Aggregate Approximation (PAA) algorithm. However, this can often lead to the loss of key information characteristics resulting in misclassification of signal types and a high risk of false alarms. This paper proposes a novel methodology based on SAX for generating more accurate symbolic representations, called Self-Organising Symbolic Aggregate Approximation (SOSAX). Data reduction is achieved through the application of an optimised PAA algorithm, Self-Organising Piecewise Aggregate Approximation (SOPAA). The approach is validated through the classification of electrocardiogram (ECG) signals where it is shown to outperform standard SAX in terms of inter-class separation and intra-class distance of signal types

Self-Organizing Piecewise Aggregate Approximation algortihm for intelligent detection and diagnosis of heart conditions

Electrocardiogram (ECG) signal classification is a recognized method for automated detection and diagnosis of heart abnormalities. This is typically achieved through dimensionality reduction techniques and feature extraction followed by signal classification using various machine learning algorithms. Although some algorithms can yield accurate results, they can be computationally demanding meaning that mobile analysis is difficult. Furthermore, discrete changes in signal characteristics, often exhibited as an early indication of the onset of heart abnormalities, can be lost in the dimensionality reduction process leading to misclassification of signal types. This paper presents a new dimensionality reduction algorithm, based on Piecewise Aggregate Approximation (PAA), called Self-Organizing Piecewise Aggregate Approximation (SOPAA) that is able to determine optimum PAA parameters based on signal characteristics within individual ECG data sets. This leads to more accurate and compact representations of ECG signals, improved classification of signal types and improved abnormality detection and diagnosis. In this work, ECG data from 99 patients exhibiting 3 different heart conditions are analyzed. Signals are discretized using both PAA and SOPAA and classified using the k-means clustering algorithm. It is shown that the SOPAA algorithm outperforms standard PAA by correctly classifying 19.7% more patients

Novelty detection based on extensions of GMMs for industrial gas turbines

The paper applies the application of Gaussian mixture models (GMMs) for operational pattern discrimination and novelty/fault detection for an industrial gas turbine (IGT). Variational Bayesian GMM (VBGMM) is used to automatically cluster operational data into steady-state and transient responses, where extraction of steady-state data is an important pre-processing scenario for fault detection. Important features are extracted from steady-state data, which are then fingerprinted to show any anomalies of patterns which may be due to machine faults. Field data measurements from vibration sensors are used to show that the extensions of GMMs provide a useful tool for machine condition monitoring, fault detection and diagnostics in the field. Through the use of experimental trials on IGTs, it is shown that GMM is particularly useful for the detection of emerging faults especially where there is a lack of knowledge of machine fault patterns

Operational pattern analysis for predictive maintenance scheduling of industrial systems

The paper presents a method to identify the operational usage patterns for industrial systems. Specifically, power measurements from an industrial gas turbine generator are studied. A fast Fourier transform (FFT) and image segmentation is used to develop an intuitive representation of operation. A spectrogram is adopted to study the average usage through the use of spectral power indices, with singular spectral analysis (SSA) applied for operational trend extraction. Through use of these techniques, two fundamental inputs for predictive maintenance scheduling viz. the users behaviour with regard to long-term unit startups patterns, and the duty cycle of power requirements, can be readily identified

Neutrophil count prediction for personalized drug dosing in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer that usually affects children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction, intensification, and maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts which can lead to a condition known as neutropenia. This carries a risk of secondary infection and has been linked to 60% ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis and is based upon clinical judgment and experience of the medical professionals involved. Conceived as a decision support aid for clinicians then, presented are the results of a machine learning technique that predicts neutrophil counts one or more weeks ahead using data from ALL blood test results and 6-MP dosing. In this work, a model is trained and validated using data from a single female ALL patient’s maintenance phase. The prediction error is found to be typically within +/- 290/microL at one week and within +/- 820/microL for a 14 day prediction

Linking healthcare associated norovirus outbreaks: a molecular epidemiologic method for investigating transmission.

BACKGROUND: Noroviruses are highly infectious pathogens that cause gastroenteritis in the community and in semi-closed institutions such as hospitals. During outbreaks, multiple units within a hospital are often affected, and a major question for control programs is: are the affected units part of the same outbreak or are they unrelated transmission events? In practice, investigators often assume a transmission link based on epidemiological observations, rather than a systematic approach to tracing transmission.Here, we present a combined molecular and statistical method for assessing:1) whether observed clusters provide evidence of local transmission and2) the probability that anecdotally|linked outbreaks truly shared a transmission event. METHODS: 76 healthcare associated outbreaks were observed in an active and prospective surveillance scheme of 15 hospitals in the county of Avon, England from April 2002 to March 2003. Viral RNA from 64 out of 76 specimens from distinct outbreaks was amplified by reverse transcription-PCR and was sequenced in the polymerase (ORF 1) and capsid (ORF 2) regions. The genetic diversity, at the nucleotide level, was analysed in relation to the epidemiological patterns. RESULTS: Two out of four genetic and epidemiological clusters of outbreaks were unlikely to have occurred by chance alone, thus suggesting local transmission. There was anecdotal epidemiological evidence of a transmission link among 5 outbreaks pairs. By combining this epidemiological observation with viral sequence data, the evidence of a link remained convincing in 3 of these pairs. These results are sensitive to prior beliefs of the strength of epidemiological evidence especially when the outbreak strains are common in the background population. CONCLUSION: The evidence suggests that transmission between hospitals units does occur. Using the proposed criteria, certain hypothesized transmission links between outbreaks were supported while others were refuted. The combined molecular/epidemiologic approach presented here could be applied to other viral populations and potentially to other pathogens for a more thorough view of transmission