139 research outputs found
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
It is important to identify the change point of a system's health status,
which usually signifies an incipient fault under development. The One-Class
Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly
detection and hence could be used for identifying change points; however, it is
sometimes difficult to obtain a good OC-SVM model that can be used on sensor
measurement time series to identify the change points in system health status.
In this paper, we propose a novel approach for calibrating OC-SVM models. The
approach uses a heuristic search method to find a good set of input data and
hyperparameters that yield a well-performing model. Our results on the C-MAPSS
dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in
detecting change point in time series with fewer training data, compared to
state-of-the-art deep learning approaches. In our case study, the OC-SVM
calibrated by the proposed model is shown to be useful especially in scenarios
with limited amount of training data
Enhancement of broadband entangled two-photon absorption by resonant spectral phase flips
Broadband energy-time entanglement can be used to enhance the rate of
two-photon absorption (TPA) by combining a precise two-photon resonance with a
very short coincidence time. Because of this short coincidence time, broadband
TPA is not sensitive to the spectrum of intermediate levels, making it the
optimal choice when the intermediate transitions are entirely virtual. In the
case of distinct intermediate resonances, it is possible to enhance TPA by
introducing a phase dispersion that matches the intermediate resonances. Here,
we consider the effects of a phase flip in the single photon spectrum, where
the phases of all frequencies above a certain frequency are shifted by half a
wavelength relative to the frequencies below this frequency. The frequency at
which the phase is flipped can then be scanned to reveal the position of
intermediate resonances. We find that a resonant phase flip maximizes the
contributions of the asymmetric imaginary part of the dispersion that
characterizes a typical resonance, resulting in a considerable enhancement of
the TPA rate. Due to the bosonic symmetry of TPA, the enhancement is strongest
when the resonance occurs when the frequency difference of the two photons is
much higher than the linewidth of the resonance. Our results indicate that
broadband entangled TPA with spectral phase flips may be suitable for
phase-sensitive spectroscopy at the lower end of the spectrum where direct
photon detection is difficult.Comment: 10 pages,6 figure
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Early detection of incipient faults is of vital importance to reducing
maintenance costs, saving energy, and enhancing occupant comfort in buildings.
Popular supervised learning models such as deep neural networks are considered
promising due to their ability to directly learn from labeled fault data;
however, it is known that the performance of supervised learning approaches
highly relies on the availability and quality of labeled training data. In
Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient
fault data has posed a major challenge to applying these supervised learning
techniques to commercial buildings. To overcome this challenge, this paper
proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised
learning pipeline, so that the resulting neural network is able to detect and
diagnose unseen incipient fault examples. We also examine the proposed
MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in
indicating the most likely incipient fault types
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