A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors

Towards design of prognostics and health management solutions for maritime assets

With increase in competition between OEMs of maritime assets and operators alike, the need to maximize the productivity of an equipment and increase operational efficiency and reliability is increasingly stringent and challenging. Also, with the adoption of availability contracts, maritime OEMs are becoming directly interested in understanding the health of their assets in order to maximize profits and to minimize the risk of a system's failure. The key to address these challenges and needs is performance optimization. For this to be possible it is important to understand that system failure can induce downtime which will increase the total cost of ownership, therefore it is important by all means to minimize unscheduled maintenance. If the state of health or condition of a system, subsystem or component is known, condition-based maintenance can be carried out and system design optimization can be achieved thereby reducing total cost of ownership. With the increasing competition with regards to the maritime industry, it is important that the state of health of a component/sub-system/system/asset is known before a vessel embarks on a mission. Any breakdown or malfunction in any part of any system or subsystem on board vessel during the operation offshore will lead to large economic losses and sometimes cause accidents. For example, damages to the fuel oil system of vessel's main engine can result in huge downtime as a result of the vessel not being in operation. This paper presents a prognostic and health management (PHM) development process applied on a fuel oil system powering diesel engines typically used in various cruise and fishing vessels, dredgers, pipe laying vessels and large oil tankers. This process will hopefully enable future PHM solutions for maritime assets to be designed in a more formal and systematic way

Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Early bearing fault analysis using high frequency enveloping techniques

High frequency acceleration enveloping is one of many tools that vibration analysts have at their disposal for the diagnosis of bearing faults in rotating machinery. This technique is believed to facilitate very early detection of potential failures by detecting low amplitude repetitive impacts in frequency ranges above conventional condition monitoring. One traditional enveloping method uses a mathematical operation known as the Hilbert transform along with other signal processing procedures such as band-pass filtering and full-wave rectification. For comparison, another method uses a proprietary algorithm included in National Instruments’ LabVIEWTM add-on package: Sound and Measurement Suite. Enveloping’s inherent problem with noise introduction is also addressed herein. A controlled, three-stage fault was induced and diagnosed utilizing both acceleration enveloping methods and traditional fast Fourier transformation (FFT) described herein. A performance assessment of the enveloping process with respect to FFT as well as the performance between individual enveloping methods is presented. In summary, several high frequency acceleration enveloping methods exist that can be effective tools in detection of bearing faults earlier than FFT alone

Life Cycle Costs for Alaska Bridges

INE/AUTC 15.0