An official website of the United States government
Early fault detection in rolling element bearings is pivotal for the effective predictive maintenance of rotating machinery. Deep Learning (DL) methods have been widely studied for vibration-based bearing fault diagnostics largely because of their capability to automatically extract fault-related features from raw or processed vibration data. Although most DL models in the current literature can provide fairly accurate classification outputs, the typical diagnostic procedure is performed in an offline environment utilizing powerful computers. This centralized approach can lead to unacceptable delays in safety-critical applications and can prohibit cost-sensitive wireless data collection. Meanwhile, very few studies have reported on deploying DL models on microprocessor-based Industrial Internet of Things (IIoT) devices, where edge computing can give users a real-time evaluation of bearing health without requiring expensive computational infrastructure. This paper demonstrates an IIoT deployment of a physics-informed DL model inside a commercially available wireless vibration sensor for online health classification. The diagnostic model here is developed and trained offline, and the trained model is then deployed inside the embedded system for online prediction. We demonstrate the model’s online diagnostic performance by imitating bearing vibration signals on a vibration shaker and by performing edge computing on the embedded system mounted on the shaker.
more »
« less
Feature Extraction From Vibration Signature Required From Railroad Bearing Onboard Condition Monitoring Sensor Modules
Abstract From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other essential features from existing vibration data, eliminating the need for additional speed sensors. Specifically, the proposed method tries to extract the speed information from the signatures that are embedded in the Power Spectral Density (PSD) plot, which enables rapid real-time analysis of bearings while the train is in motion. The rapid extraction of data could be sent to a cloud accessible by train dispatchers and railcar owners for assessment of bearings and scheduling of replacements before defects reach a dangerous size. Eventually, the developed algorithm will reduce derailments and unplanned field replacements and afford rail stakeholders more cost-effective preventive maintenance.
more »
« less
- Award ID(s):
- 2401828
- PAR ID:
- 10590678
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8777-6
- Format(s):
- Medium: X
- Location:
- Columbia, South Carolina, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology.more » « less
-
Fault diagnosis of rolling bearings becomes an important research subject, where the data-driven deep learning-based techniques have been extensively exploited. While the state-of-the-art research has shown the substantial progresses in bearing fault diagnosis, they mostly were implemented upon the hypothesis that the location of bearing prone to failure already is known. Nevertheless, in actual practice many rolling bearings are installed in a complex machinery system, any of which is likely subject to fault. As such, fault diagnosis essentially is a process to achieve both fault localization and identification, which results in many fault scenarios to be handled. This will significantly degrade the fault diagnosis performance using conventional deep learning analysis. In this research, we aim to develop a new deep learning framework to address abovementioned challenge. We particularly design a hierarchical deep learning framework consisting of multiple sequentially deployed deep learning models built upon the transfer learning. This can improve the learning adequacy for a high-dimensional problem with many fault scenarios involved even under limited dataset, thereby enhancing the fault diagnosis performance. Without the prior knowledge regarding the fault location, this methodology is greatly favored by the sensor/data fusion which takes full advantage of the enriched pivot fault-related features in the measurements acquired from different accelerometers. Systematic case studies using the publicly accessible experimental rolling bearing dataset are carried out to validate this new methodology.more » « less
-
Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle to the success of this method. High speeds are essential to induce a significant amount of kinetic energy to stimulate the bridge modes of vibration. On the other hand, low speeds are necessary to collect more data and to attenuate the vibration of the vehicle due to the roughness of the road and, hence, magnify the bridge influence on the vehicle responses. This article introduces Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) as a new technique, which possesses the ability to extract bridge dynamic properties from the responses of a vehicle that passes over the bridge at high speed. Stochastic Resonance (SR) is a phenomenon where feeble information such as weak signals can be amplified through the assistance of background noise. In this study, bridge vibrations that are present in the vehicle responses when it passes over the bridge are the feeble information while the noise counts for the effect of the road roughness on the vehicle vibration. UPSR is one of the SR models that has been chosen in this study for its suitability to extract the bridge vibration. The main contributions of this article are: (1) introducing a Frequency Independent-Stochastic Resonance model known as the FI-UPSR and (2) implementing this model to extract the bridge vibration from the responses of a fast passing vehicle.more » « less
-
Cognitive Fatigue (CF) is the decline in cognitive abilities due to prolonged exposure to mentally demanding tasks. In this paper, we used gait cycle analysis, a biometric method related to human locomotion to identify cognitive fatigue in individuals. The proposed system in this paper takes two asynchronous videos of the gait of individuals to classify if they are cognitively fatigued or not. We leverage the pose estimation library OpenPose, to extract the body keypoints from the frames in the videos. To capture the spatial and temporal information of the gait cycle, a CNN-based model is used in the system to extract the embedded features which are then used to classify the cognitive fatigue level of individuals. To train and test the model, a gait dataset is built from 21 participants by collecting walking data before and after inducing cognitive fatigue using clinically used games. The proposed model can classify cognitive fatigue from the gait data of an individual with an accuracy of 81%.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/JRC2024-130045
