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