Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532550
Mustafa Caglar Guldiken, Onder Unver
Platform Independent Network-Based Health Monitoring & Diagnostic Solution for Urban Security Surveillance System and License Plate Recognition System is presented. Urban Security Surveillance System and License Plate Recognition system contains several different IP-based devices to communicate each other. The system is required to provide 24/7 service. Therefore, highly comprehensive system integration test is crucial to eliminate the problems that come from the field, and to respond the customers' expectation. Moreover, observation of the system's health status and diagnosis of any possible fault are also vital. Hence; in this paper Urban Security Health monitoring and Diagnostic System (USHDS) which works on Windows operating system is proposed as a solution for health monitoring, diagnostic and test processes for each unit and the whole country-wide urban security system used by government.
{"title":"Platform Independent Network-Based Health Monitoring & Diagnostic Solution for Urban Security Surveillance System and License Plate Recognition System","authors":"Mustafa Caglar Guldiken, Onder Unver","doi":"10.1109/AUTEST.2018.8532550","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532550","url":null,"abstract":"Platform Independent Network-Based Health Monitoring & Diagnostic Solution for Urban Security Surveillance System and License Plate Recognition System is presented. Urban Security Surveillance System and License Plate Recognition system contains several different IP-based devices to communicate each other. The system is required to provide 24/7 service. Therefore, highly comprehensive system integration test is crucial to eliminate the problems that come from the field, and to respond the customers' expectation. Moreover, observation of the system's health status and diagnosis of any possible fault are also vital. Hence; in this paper Urban Security Health monitoring and Diagnostic System (USHDS) which works on Windows operating system is proposed as a solution for health monitoring, diagnostic and test processes for each unit and the whole country-wide urban security system used by government.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532544
Aaron Radke, Sheri Cymrot, Kevin A'Heam, Aaron Wagner, Blaire Angle
This paper presents an approach for a low cost, platform-agnostic, unmanned systems anomaly detection system that learns normal operating conditions from limited data and computing resources to track deviations from those conditions over time. Machine learning and automatic anomaly detection use has exploded in the Big Data arena with the availability of large volumes of historical data and extensive computing resources. However, in the case of unmanned systems, there is typically limited historical data available and computational resources are often restricted to embedded devices similar to cell phones. We discuss the application of two algorithms for anomaly detection in this “small data” context: 1) sparse modeling and 2) T-Digest. These algorithms are also designed and chosen to perform generically across a number of target application domains with a standalone health monitoring sensor box coupled with noninvasive sensors. Acoustic and inertial sensors have been initially selected to illustrate and validate the system capability and performance.
{"title":"“Small Data” Anomaly Detection for Unmanned Systems","authors":"Aaron Radke, Sheri Cymrot, Kevin A'Heam, Aaron Wagner, Blaire Angle","doi":"10.1109/AUTEST.2018.8532544","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532544","url":null,"abstract":"This paper presents an approach for a low cost, platform-agnostic, unmanned systems anomaly detection system that learns normal operating conditions from limited data and computing resources to track deviations from those conditions over time. Machine learning and automatic anomaly detection use has exploded in the Big Data arena with the availability of large volumes of historical data and extensive computing resources. However, in the case of unmanned systems, there is typically limited historical data available and computational resources are often restricted to embedded devices similar to cell phones. We discuss the application of two algorithms for anomaly detection in this “small data” context: 1) sparse modeling and 2) T-Digest. These algorithms are also designed and chosen to perform generically across a number of target application domains with a standalone health monitoring sensor box coupled with noninvasive sensors. Acoustic and inertial sensors have been initially selected to illustrate and validate the system capability and performance.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130789180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532546
Wafa Ben Hassen, M. Kafal, E. Cabanillas
In wiring networks, on-line diagnosis aims at detecting and locating faults in live cables concurrently to their normal operation. Within this context, Multi-carrier Reftectometry (MCR), a reftectometry based method, not only succeeded in this mission but also permitted controlling the signal bandwidth and thus avoiding false alarms. However, it has shown to suffer from signal loss related to wiring system's attenuation, coupling and network-topology complexity. On the other hand, Time Reversal (TR) signal processing recently adapted with efficiency to wire diagnosis, has proven to boost the performance with an increased network response's complexity accompanied with a higher detection gain. In this paper, we propose a fusion between TR and MCR in a TR Multi-Carrier Reflectometry (TRMCR) method aiming at maximizing the coverage of the online diagnosis. Simulation and experimental results demonstrate that TRMCR permits to increase the peak's signature at detected impedance discontinuities in a network under test (NUT) when compared to what the standard MCR produce.
{"title":"Time Reversal Applied to Multi-Carrier Reflectometry for On-line Diagnosis in Complex Wiring Systems","authors":"Wafa Ben Hassen, M. Kafal, E. Cabanillas","doi":"10.1109/AUTEST.2018.8532546","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532546","url":null,"abstract":"In wiring networks, on-line diagnosis aims at detecting and locating faults in live cables concurrently to their normal operation. Within this context, Multi-carrier Reftectometry (MCR), a reftectometry based method, not only succeeded in this mission but also permitted controlling the signal bandwidth and thus avoiding false alarms. However, it has shown to suffer from signal loss related to wiring system's attenuation, coupling and network-topology complexity. On the other hand, Time Reversal (TR) signal processing recently adapted with efficiency to wire diagnosis, has proven to boost the performance with an increased network response's complexity accompanied with a higher detection gain. In this paper, we propose a fusion between TR and MCR in a TR Multi-Carrier Reflectometry (TRMCR) method aiming at maximizing the coverage of the online diagnosis. Simulation and experimental results demonstrate that TRMCR permits to increase the peak's signature at detected impedance discontinuities in a network under test (NUT) when compared to what the standard MCR produce.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532506
A. Sguigna
Poor system reliability, combined with frequent failures of Built-In Test (BIT), may cause crew to undertake missions with undetected faults. Further, the need for rapid field repair, combined with line-replaceable unit (LRU) endemic fault isolation, dictates a new approach to system test. The use of JTAG-based boundary-scan test (BST), embedded on-board without the need for external physical hardware probes, cabling and fixturing, is described to address this issue. This paper details the application of JTAG for BIT, Test Access Port (TAP) controller firmware requirements, BST library Application Program Interface (API), and hardware design requirements.
{"title":"JTAG/Boundary Scan for Built-In Test","authors":"A. Sguigna","doi":"10.1109/AUTEST.2018.8532506","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532506","url":null,"abstract":"Poor system reliability, combined with frequent failures of Built-In Test (BIT), may cause crew to undertake missions with undetected faults. Further, the need for rapid field repair, combined with line-replaceable unit (LRU) endemic fault isolation, dictates a new approach to system test. The use of JTAG-based boundary-scan test (BST), embedded on-board without the need for external physical hardware probes, cabling and fixturing, is described to address this issue. This paper details the application of JTAG for BIT, Test Access Port (TAP) controller firmware requirements, BST library Application Program Interface (API), and hardware design requirements.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532520
A. Ozkan
In this study, the approach of RATIL (Radar Antenna Test In the Loop) is proposed for measuring the antenna characterization, mono pulse pattern, performance of direction finding accuracy in the scope of radar antenna far field tests. RATIL is the concept for a radar which is using the internal self-resource components such like receiver, transmitter, digital signal processor etc., without using the external test equipment sources to operate the antenna tests. Thus, with this method, the test reliability and measurement accuracy is being improved, test duration is being shortened and a cost efficient solution is being achieved.
{"title":"Radar Antenna Test In the Loop Far-Field Measurements","authors":"A. Ozkan","doi":"10.1109/AUTEST.2018.8532520","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532520","url":null,"abstract":"In this study, the approach of RATIL (Radar Antenna Test In the Loop) is proposed for measuring the antenna characterization, mono pulse pattern, performance of direction finding accuracy in the scope of radar antenna far field tests. RATIL is the concept for a radar which is using the internal self-resource components such like receiver, transmitter, digital signal processor etc., without using the external test equipment sources to operate the antenna tests. Thus, with this method, the test reliability and measurement accuracy is being improved, test duration is being shortened and a cost efficient solution is being achieved.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122399332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532541
N. Mclellan, A. Shilling
Programmatic improvements can be achieved through the incorporation of “lessons learned” from preceding projects in the acquisition of Consolidated Automated Support System (CASS) Family of Testers (FoT) Operational Test Program Sets (OTPS). The application of the Systems Engineering Technical Review (SETR) process to CASS FoT OTPSs presents unique challenges. Adhering to policy and minimizing impact to total lifecycle costs (TLC) have differing goals. However, lessons can be extracted from previous CASS OTPS procurements implementing SETR while minimizing the competing effects of the two. The SETR process is NAVAIR's procedure to perform system engineering reviews for Naval Aviation products. This process has been implemented on CASS FoT OTPS procurements for a multitude of reasons including: increased risk management, ensuring product confidence, and program/project uniformity. SETR provides an assessment of the emerging design against the overall objective of promoting a well-managed development effort leading to a system that meets programmatic requirements while still providing the system performance required to support mission needs. It also offers insight into project progress while providing a layer of independent review at programmatic milestones. This paper will discuss the experience of implementing SETR on CASS FoT OTPS procurements. It will summarize some lessons learned with key areas of focus to include: obstacles, useful elements, and specific examples of implementation from recent CASS OTPS programs. The focus will be on a few key elements of SETR that are especially important for CASS OTPSs: stakeholder participation, checklist tailoring, requirements tracking, and risk management. These topics will be covered from the point of view of government acquisition, but there will be valuable insights for TPS developers. The intent is to allow future CASS FoT OTPS development projects to effectively implement SETR while minimizing the cost and schedule impacts of policy, thereby reducing TLC, and increasing speed to the fleet.
{"title":"Lessons Learned in Utilizing the SETR Process in the Procurement of TPSs on the CASS Family of Testers","authors":"N. Mclellan, A. Shilling","doi":"10.1109/AUTEST.2018.8532541","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532541","url":null,"abstract":"Programmatic improvements can be achieved through the incorporation of “lessons learned” from preceding projects in the acquisition of Consolidated Automated Support System (CASS) Family of Testers (FoT) Operational Test Program Sets (OTPS). The application of the Systems Engineering Technical Review (SETR) process to CASS FoT OTPSs presents unique challenges. Adhering to policy and minimizing impact to total lifecycle costs (TLC) have differing goals. However, lessons can be extracted from previous CASS OTPS procurements implementing SETR while minimizing the competing effects of the two. The SETR process is NAVAIR's procedure to perform system engineering reviews for Naval Aviation products. This process has been implemented on CASS FoT OTPS procurements for a multitude of reasons including: increased risk management, ensuring product confidence, and program/project uniformity. SETR provides an assessment of the emerging design against the overall objective of promoting a well-managed development effort leading to a system that meets programmatic requirements while still providing the system performance required to support mission needs. It also offers insight into project progress while providing a layer of independent review at programmatic milestones. This paper will discuss the experience of implementing SETR on CASS FoT OTPS procurements. It will summarize some lessons learned with key areas of focus to include: obstacles, useful elements, and specific examples of implementation from recent CASS OTPS programs. The focus will be on a few key elements of SETR that are especially important for CASS OTPSs: stakeholder participation, checklist tailoring, requirements tracking, and risk management. These topics will be covered from the point of view of government acquisition, but there will be valuable insights for TPS developers. The intent is to allow future CASS FoT OTPS development projects to effectively implement SETR while minimizing the cost and schedule impacts of policy, thereby reducing TLC, and increasing speed to the fleet.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122860630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532549
Christopher J. Guerra, C. Camargo
The vulnerability footprint for complex systems includes many potential vectors for compromising the data integrity, system functionality, flight worthiness, and availability. The point of intrusion could occur years prior to fielding the system through the introduction of hardware with “hooks” for a future attack. For support equipment with common operating systems, the footprint available to those with hostile intent is greater. The quantity of users which have contact or near contact with the support equipment amplifies the vulnerability of the complex system. Not all support equipment has a digital or software component. While purely mechanical fixtures have a lower cybersecurity risk, they are not immune. Often they are manufactured or refurbished using automatic test equipment which could be affected resulting an imperceptible defect in the support equipment's performance. We describe a methodology to measure and assess the cybersecurity risk of complex system or a fleet of complex systems in response to the support equipment footprint, which interfaces with the system. This approach combines information from two key databases. The first database characterizes the information flow and interfaces between the subsystems to include the support equipment. The second database describes the critical, open-ended interface points for an attack against the support equipment. The critical parameters can include the type of operating system, the number of exposed ports and their types, and the presence of wireless interfaces. We define impact parameters for the case where a subsystem is compromised. Similarly, we define risk parameters for the support equipment based on criteria which is a function of the susceptibility of the technology employed within the support equipment. As in reliability analyses, we construct a network of the relationships between the subsystems and the support equipment. We can compute the two-dimensional risk-impact relationship for a given support equipment item to the subsystem or to the complete system. This approach can be extended to compute a fleet level risk and impact for all of the support equipment.
{"title":"Measuring and Assessing the Cybersecurity Risk of Support Equipment to Complex Systems","authors":"Christopher J. Guerra, C. Camargo","doi":"10.1109/AUTEST.2018.8532549","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532549","url":null,"abstract":"The vulnerability footprint for complex systems includes many potential vectors for compromising the data integrity, system functionality, flight worthiness, and availability. The point of intrusion could occur years prior to fielding the system through the introduction of hardware with “hooks” for a future attack. For support equipment with common operating systems, the footprint available to those with hostile intent is greater. The quantity of users which have contact or near contact with the support equipment amplifies the vulnerability of the complex system. Not all support equipment has a digital or software component. While purely mechanical fixtures have a lower cybersecurity risk, they are not immune. Often they are manufactured or refurbished using automatic test equipment which could be affected resulting an imperceptible defect in the support equipment's performance. We describe a methodology to measure and assess the cybersecurity risk of complex system or a fleet of complex systems in response to the support equipment footprint, which interfaces with the system. This approach combines information from two key databases. The first database characterizes the information flow and interfaces between the subsystems to include the support equipment. The second database describes the critical, open-ended interface points for an attack against the support equipment. The critical parameters can include the type of operating system, the number of exposed ports and their types, and the presence of wireless interfaces. We define impact parameters for the case where a subsystem is compromised. Similarly, we define risk parameters for the support equipment based on criteria which is a function of the susceptibility of the technology employed within the support equipment. As in reliability analyses, we construct a network of the relationships between the subsystems and the support equipment. We can compute the two-dimensional risk-impact relationship for a given support equipment item to the subsystem or to the complete system. This approach can be extended to compute a fleet level risk and impact for all of the support equipment.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127210427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532533
B. Stasonis, K. Moore
When an engineer is designing a functional test system, it is normally the goal to design and integrate the best system in terms of measurement accuracy, throughput, and budget. Too often switching is the last section to be added. How switching is implemented in your test strategy can affect accuracy and repeatability. It is important to note that you do not need to keep all instrumentation and switching in the same platform. In this paper, we will show an overview of the three most popular platforms used for switching today, the advantages of each in various switching applications, and provide some basic questions to ask as you integrate any test system. Our goals today are simple – we want to make you “dangerous” in terms of switching platforms. By that we mean that we can't make you an expert by reading our paper, but we will arm you with enough knowledge to research for more details. We assume the reader understands the practices of good Switching system design.
{"title":"Choosing the Right Platform for Switching: PXI, USB or LXI?","authors":"B. Stasonis, K. Moore","doi":"10.1109/AUTEST.2018.8532533","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532533","url":null,"abstract":"When an engineer is designing a functional test system, it is normally the goal to design and integrate the best system in terms of measurement accuracy, throughput, and budget. Too often switching is the last section to be added. How switching is implemented in your test strategy can affect accuracy and repeatability. It is important to note that you do not need to keep all instrumentation and switching in the same platform. In this paper, we will show an overview of the three most popular platforms used for switching today, the advantages of each in various switching applications, and provide some basic questions to ask as you integrate any test system. Our goals today are simple – we want to make you “dangerous” in terms of switching platforms. By that we mean that we can't make you an expert by reading our paper, but we will arm you with enough knowledge to research for more details. We assume the reader understands the practices of good Switching system design.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128179848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532529
R. Shannon, Gregory Zucaro, J. Tallent, Vontrelle Collins, John Carswell
Industry-produced printed circuit boards (PCBs) used by the United States Navy and Marine Corps are typically coated with a layer of “conformal coating” made of silicone or polyurethane in order to protect electrical and electronic components on the board. Conformal coating has to be removed every time board troubleshooting and maintenance are performed, and must be reapplied after board maintenance is complete. This can be an expensive and time-consuming process. This paper describes an effort to develop a non-contact solution to detect failed components on a PCB without having first to remove the conformal coating. This patent-pending technique detects density changes in the physical makeup of circuit board components due to failure. By analyzing ultrasonic reflections from the components at 2MHz, the authors were able to distinguish between working components and failed components with varying degrees of accuracy. The authors applied this technique to $1KOmega$ resistors and three types of transistor-to-transistor logic (TTL) integrated circuits (ICs). Overvoltage faults were induced in these components in order to generate observable density changes. To reduce human error, a measurement rig was built which incorporated an automated X-Y-Z plotter system, in order to process dozens of components at a time without human interaction. The data gathered by this system was processed to isolate only the acoustic reflections of components on a circuit board. Time-domain and frequency-domain features were then extracted. These features were used to train neural networks to distinguish between working components and components with over-voltage faults that were not readily observable by eye. Each type of component or chip needed to have its own associated trained neural network. For $1KOmega$ resistors, the system has demonstrated seventy to eighty percent accuracy in distinguishing components with over-voltage faults. For two of the TTL ICs, eighty to eighty-five percent accuracy has been achieved. For one IC type, a fifty-five percent accuracy was measured. The authors have demonstrated that low-cost acoustic measurements in the megahertz range can be used to detect failures in ICs and other common circuit board components.
{"title":"A System for Detecting Failed Electronics Using Acoustics","authors":"R. Shannon, Gregory Zucaro, J. Tallent, Vontrelle Collins, John Carswell","doi":"10.1109/AUTEST.2018.8532529","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532529","url":null,"abstract":"Industry-produced printed circuit boards (PCBs) used by the United States Navy and Marine Corps are typically coated with a layer of “conformal coating” made of silicone or polyurethane in order to protect electrical and electronic components on the board. Conformal coating has to be removed every time board troubleshooting and maintenance are performed, and must be reapplied after board maintenance is complete. This can be an expensive and time-consuming process. This paper describes an effort to develop a non-contact solution to detect failed components on a PCB without having first to remove the conformal coating. This patent-pending technique detects density changes in the physical makeup of circuit board components due to failure. By analyzing ultrasonic reflections from the components at 2MHz, the authors were able to distinguish between working components and failed components with varying degrees of accuracy. The authors applied this technique to $1KOmega$ resistors and three types of transistor-to-transistor logic (TTL) integrated circuits (ICs). Overvoltage faults were induced in these components in order to generate observable density changes. To reduce human error, a measurement rig was built which incorporated an automated X-Y-Z plotter system, in order to process dozens of components at a time without human interaction. The data gathered by this system was processed to isolate only the acoustic reflections of components on a circuit board. Time-domain and frequency-domain features were then extracted. These features were used to train neural networks to distinguish between working components and components with over-voltage faults that were not readily observable by eye. Each type of component or chip needed to have its own associated trained neural network. For $1KOmega$ resistors, the system has demonstrated seventy to eighty percent accuracy in distinguishing components with over-voltage faults. For two of the TTL ICs, eighty to eighty-five percent accuracy has been achieved. For one IC type, a fifty-five percent accuracy was measured. The authors have demonstrated that low-cost acoustic measurements in the megahertz range can be used to detect failures in ICs and other common circuit board components.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AUTEST.2018.8532554
D. Lowenstein, C. Slater
Understanding what, where, and how test assets are being used has always been an arduous, if not, impossible task. With the advances in real time data, IoT (internet of Things), and big data, which are combined to build the foundation of Industry 4.0 (I4.0), we now have access to real-time actionable information at our finger tips. Not only can what, where, and how be obtained, barriers to intelligent systems and health/prognostics can be almost nonexistent. Organizations that have moved in this direction have seen increased utilization, uptime, flexibility, effectiveness and efficiency with dramatic decreases in cost of ownership, cost of test, ramp up time and diagnostics, and repair for both the systems and products built. Even secondary factors like system accuracy (or uncertainty), asset reuse, technology refresh, and gage R&R (repeatability and reproducibility) have shown positive impacts on overall test strategies and overall business results. Even with all these benefits, the barriers to entry to this new world can be massive. Not only are there the normal issues around change management, ingrained organizational culture, money and regulations, just as important are the changes needed in overall philosophy and operations of every part of a programs/product's life cycle. In the world of real-time data, there is no more throwing R&D designs over the proverbial wall to manufacturing - the wall is replaced with a two-way stream of real-time actionable data and feedback. It also breaks down all the walls between different operations in production, allowing data to be used for functions like; diagnostics and repair, allowing real-time measurement trending to measure uncertainty, optimizing assets to maximize utilization, monitoring instrument health, and removing the multitude of areas that manual processes cause “human” errors. These are just some of the benefits that come from developing, collecting and utilizing a real-time data strategy throughout a product's life cycle. A robust strategy benefits and strengthens virtually all parts of a business including development, production, support, marketing, supply chain, finance, and order fulfillment. This paper will explore the multi-faceted world of real-time data and how it relates to test. It will outline the short and long-term benefits, the cultural and strategic changes needed, the direct benefits to test strategies, its influence throughout the total product life cycle and its overall impact on cost, time and scope. In addition, it will lay out a practical way to get started on the journey to move forward on not only a strategy, but also an implementation plan.
{"title":"Management of Test Utilization, Optimization, and Health through Real-Time Data","authors":"D. Lowenstein, C. Slater","doi":"10.1109/AUTEST.2018.8532554","DOIUrl":"https://doi.org/10.1109/AUTEST.2018.8532554","url":null,"abstract":"Understanding what, where, and how test assets are being used has always been an arduous, if not, impossible task. With the advances in real time data, IoT (internet of Things), and big data, which are combined to build the foundation of Industry 4.0 (I4.0), we now have access to real-time actionable information at our finger tips. Not only can what, where, and how be obtained, barriers to intelligent systems and health/prognostics can be almost nonexistent. Organizations that have moved in this direction have seen increased utilization, uptime, flexibility, effectiveness and efficiency with dramatic decreases in cost of ownership, cost of test, ramp up time and diagnostics, and repair for both the systems and products built. Even secondary factors like system accuracy (or uncertainty), asset reuse, technology refresh, and gage R&R (repeatability and reproducibility) have shown positive impacts on overall test strategies and overall business results. Even with all these benefits, the barriers to entry to this new world can be massive. Not only are there the normal issues around change management, ingrained organizational culture, money and regulations, just as important are the changes needed in overall philosophy and operations of every part of a programs/product's life cycle. In the world of real-time data, there is no more throwing R&D designs over the proverbial wall to manufacturing - the wall is replaced with a two-way stream of real-time actionable data and feedback. It also breaks down all the walls between different operations in production, allowing data to be used for functions like; diagnostics and repair, allowing real-time measurement trending to measure uncertainty, optimizing assets to maximize utilization, monitoring instrument health, and removing the multitude of areas that manual processes cause “human” errors. These are just some of the benefits that come from developing, collecting and utilizing a real-time data strategy throughout a product's life cycle. A robust strategy benefits and strengthens virtually all parts of a business including development, production, support, marketing, supply chain, finance, and order fulfillment. This paper will explore the multi-faceted world of real-time data and how it relates to test. It will outline the short and long-term benefits, the cultural and strategic changes needed, the direct benefits to test strategies, its influence throughout the total product life cycle and its overall impact on cost, time and scope. In addition, it will lay out a practical way to get started on the journey to move forward on not only a strategy, but also an implementation plan.","PeriodicalId":384058,"journal":{"name":"2018 IEEE AUTOTESTCON","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129239118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：