Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem for model-based anomaly detection. We introduce a long short-term memory-based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution by introducing a progress-based varying prior. Our LSTM-VAE-based detector reports an anomaly when a reconstruction-based anomaly score is higher than a state-based threshold. For evaluations with 1555 robot-assisted feeding executions, including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve of 0.8710 than 5 other baseline detectors from the literature. We also show the variational autoencoding and state-based thresholding are effective in detecting anomalies from 17 raw sensory signals without significant feature engineering effort.

A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder

A survey on ECG analysis

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

Failure Prognosis and Applications—A Survey of Recent Literature

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications.

An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals

Traditional approaches for obstructive sleep apnea (OSA) diagnosis are apt to using multiple channels of physiological signals to detect apnea events by dividing the signals into equal-length segments, which may lead to incorrect apnea event detection and weaken the performance of OSA diagnosis. This paper proposes an automatic-segmentation-based screening approach with the single channel of Electrocardiogram (ECG) signal for OSA subject diagnosis, and the main work of the proposed approach lies in three aspects: (i) an automatic signal segmentation algorithm is adopted for signal segmentation instead of the equal-length segmentation rule; (ii) a local median filter is improved for reduction of the unexpected RR intervals before signal segmentation; (iii) the designed OSA severity index and additional admission information of OSA suspects are plugged into support vector machine (SVM) for OSA subject diagnosis. A real clinical example from PhysioNet database is provided to validate the proposed approach and an average accuracy of 97.41% for subject diagnosis is obtained which demonstrates the effectiveness for OSA diagnosis.

An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram

Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.

Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

To prevent unexpected failures of complex engineering systems, multiple sensors have been widely used to simultaneously monitor the degradation process and make inference about the remaining useful life in real time. As each of the sensor signals often contains partial and dependent information, data-level fusion techniques have been developed that aim to construct a health index via the combination of multiple sensor signals. While the existing data-level fusion approaches have shown a promise for degradation modeling and prognostics, they are limited by only considering a linear fusion function. Such a linear assumption is usually insufficient to accurately characterize the complicated relations between multiple sensor signals and the underlying degradation process in practice, especially for complex engineering systems considered in this study. To address this issue, this study fills the literature gap by integrating kernel methods into the data-level fusion approaches to construct a health index for better characterizing the degradation process of the system. Through selecting a proper kernel function, the nonlinear relation between multiple sensor signals and the underlying degradation process can be captured. As a result, the constructed health index is expected to perform better in prognosis than existing data-level fusion methods that are based on the linear assumption. In fact, the existing data-level fusion models turn out to be only a special case of the proposed method. A case study based on the degradation signals of aircraft gas turbine engines is conducted and finally shows the developed health index by using the proposed method is insensitive for missing data and leads to an improved prognostic performance.

Integration of Data-Level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis

Operating units, in practice, often suffer from multiple modes of failure, and each failure mode has a distinct influence on the service life cycle path of a unit. The rapid development of sensor and communication technologies has enabled multiple sensors to simultaneously monitor and track the health status of a unit in real time. However, one challenging question that remains to be resolved is how to leverage data from multiple sensors for better degradation modeling and prognostic analysis, especially when there are multiple failure modes. Currently, many of the existing approaches in prognostics either (a) fail to capture the dependency between sensors and instead focus on analyzing each sensor independently or (b) fail to incorporate the failure-mode diagnosis for better degradation modeling and prognostics during condition monitoring. To address the limitations in the existing literature, we propose a data-level fusion methodology to construct a composite failure-mode index, named FM-INDEX, ...

Changyue Song

Papers

An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals

An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram

Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

Integration of Data-Level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis

A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes