Collaborating Authors

Domain Adaptation in Robot Fault Diagnostic Systems Artificial Intelligence

Industrial robots play an important role in manufacturing process. Since robots are usually set up in parallel-serial settings, breakdown of a single robot has a negative effect on the entire manufacturing process in that it slows down the process. Therefore, fault diagnostic systems based on the internal signals of robots have gained a lot of attention as essential components of the services provided for industrial robots. The current work in fault diagnostic algorithms extract features from the internal signals of the robot while the robot is healthy in order to build a model representing the normal robot behavior. During the test, the extracted features are compared to the normal behavior for detecting any deviation. The main challenge with the existing fault diagnostic algorithms is that when the task of the robot changes, the extracted features differ from those of the normal behavior. As a result, the algorithm raises false alarm. To eliminate the false alarm, fault diagnostic algorithms require the model to be retrained with normal data of the new task. In this paper, domain adaptation, {\it a.k.a} transfer learning, is used to transfer the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate the false alarm. The results of the proposed algorithm on real dataset show the ability of the domain adaptation in distinguishing the operation change from the mechanical condition change.

Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review Machine Learning

In this survey paper, we systematically summarize the current literature on studies that apply machine learning (ML) and data mining techniques to bearing fault diagnostics. Conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to detecting and categorizing bearing faults since the last decade, while the application of deep learning (DL) methods has sparked great interest in both the industry and academia in the last five years. In this paper, we will first review the conventional ML methods, before taking a deep dive into the latest developments in DL algorithms for bearing fault applications. Specifically, the superiority of the DL based methods over the conventional ML methods are analyzed in terms of metrics directly related to fault feature extraction and classifier performances; the new functionalities offered by DL techniques that cannot be accomplished before are also summarized. In addition, to obtain a more intuitive insight, a comparative study is performed on the classifier performance and accuracy for a number of papers utilizing the open source Case Western Reserve University (CWRU) bearing data set. Finally, based on the nature of the time-series 1-D data obtained from sensors monitoring the bearing conditions, recommendations and suggestions are provided to applying DL algorithms on bearing fault diagnostics based on specific applications, as well as future research directions to further improve its performance.

Solving Strong-Fault Diagnostic Models by Model Relaxation

AAAI Conferences

In Model-Based Diagnosis (MBD), the problem of computing a diagnosis in a strong-fault model (SFM) is computationally much harder than in a weak-fault model (WFM). For example, in propositional Horn models, computing the first minimal diagnosis in a weak-fault model (WFM) is in P but is NP-hard for strong-fault models. As a result, SFM problems of practical significance have not been studied in great depth within the MBD community. In this paper we describe an algorithm that renders the problem of computing a diagnosis in several important SFM subclasses no harder than a similar computation in a WFM. We propose an approach for efficiently computing minimal diagnoses for these subclasses of SFM that extends existing conflict-based algorithms like GDE (Sherlock) and CDA*. Experiments on ISCAS85 combinational circuits show (1) inference speedups with CDA* of up to a factor of 8, and (2) an average of 28% reduction in the average conflict size, at the price of an extra low-polynomial-time consistency check for a candidate diagnosis.

California Earthquakes: San Diego-Los Angeles Fault Could Cause 7.4-Magnitude Temblor

International Business Times

As if the San Andreas fault wasn't worrying enough already, California has another big potential earthquake problem to be concerned about. A study published Tuesday identified a new fault system that runs from San Diego to Los Angeles that is capable of causing an earthquake of up to 7.4-magnitude. Led by the Scripps Institution of Oceanography at the University of California, San Diego, the study found that faults in Newport-Inglewood and Rose Canyon, earlier thought to be two separate faults, were actually part of the same fault system that runs both on land and offshore, from San Diego Bay to Seal Beach, Orange County, and then moving on shore to the Los Angeles Basin. Lead author of the study Valerie Sahakian, who studied at Scripps and is now at the U.S. Geological Survey, said in a statement: "This system is mostly offshore but never more than four miles from the San Diego, Orange County, and Los Angeles County coast. Even if you have a high 5- or low 6-magnitude earthquake, it can still have a major impact on those regions, which are some of the most densely populated in California."

The Diagnostic Competitions

AI Magazine

This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms. Diagnostic algorithms (DAs) (1) detect malfunctioning systems, (2) isolate the faulty component or components that cause the malfunction, and possibly (3) repair the system to restore its functionality. The fundamental challenge of diagnosis is that the system is only partially observable. Therefore, diagnostic algorithms must reason backwards from symptoms to causes.