5 research outputs found
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis
Recurrent auto-encoder model summarises sequential data through an encoder
structure into a fixed-length vector and then reconstructs the original
sequence through the decoder structure. The summarised vector can be used to
represent time series features. In this paper, we propose relaxing the
dimensionality of the decoder output so that it performs partial
reconstruction. The fixed-length vector therefore represents features in the
selected dimensions only. In addition, we propose using rolling fixed window
approach to generate training samples from unbounded time series data. The
change of time series features over time can be summarised as a smooth
trajectory path. The fixed-length vectors are further analysed using additional
visualisation and unsupervised clustering techniques. The proposed method can
be applied in large-scale industrial processes for sensors signal analysis
purpose, where clusters of the vector representations can reflect the operating
states of the industrial system.Comment: Accepted paper at the 19th International Conference on Engineering
Applications of Neural Networks (EANN 2018
Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data
We propose Deep Dict, a deep learning-based lossy time series compressor
designed to achieve a high compression ratio while maintaining decompression
error within a predefined range. Deep Dict incorporates two essential
components: the Bernoulli transformer autoencoder (BTAE) and a distortion
constraint. BTAE extracts Bernoulli representations from time series data,
reducing the size of the representations compared to conventional autoencoders.
The distortion constraint limits the prediction error of BTAE to the desired
range. Moreover, in order to address the limitations of common regression
losses such as L1/L2, we introduce a novel loss function called quantized
entropy loss (QEL). QEL takes into account the specific characteristics of the
problem, enhancing robustness to outliers and alleviating optimization
challenges. Our evaluation of Deep Dict across ten diverse time series datasets
from various domains reveals that Deep Dict outperforms state-of-the-art lossy
compressors in terms of compression ratio by a significant margin by up to
53.66%.Comment: 6 pages, 13 figures, IEEE International Conference on Communications
(ICC) 202
Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review
The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed
Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios
xvii, 218 p.The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario