22 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
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
Spectrally resolved two-photon interference in a modified Hong-Ou-Mandel interferometer
A modified Hong-Ou-Mandel(HOM) interference reveals that the two-photon
interference phenomenon can be explained only by the concept of a two-photon
wave packet rather than the single-photon one. Previously, the measurements for
such interference were usually performed in the time domain where the spectral
information of the involved photons was integrated and lost during the
measurement. Here, we theoretically explore the spectrally resolved two-photon
interference for the modified HOM interferometer both in the cases of CW pump
and pulse pump. It is found that, in the CW-pumped case, a one-dimensional (1D)
temporal interferogram can be directly recovered by projecting a 2D spectrally
resolved interferogram at different phases, without a standard delay-scanning.
In the pulse-pumped case, the joint spectral intensity is phase-dependent and
can be modulated by the time delay along the directions of both frequency sum
and frequency difference between signal and idler photons, which may provide a
versatile way to generate high-dimensional frequency entanglement and engineer
high-dimensional quantum states. These results not only show more rich spectral
information that cannot be extracted from the time domain, but also shed new
light on a comprehensive understanding of the two-photon interference
phenomenon in the frequency domain.Comment: 13 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