scholarly journals Pushing Failure Mode Stimulus to Overcome the Limitation/Boundaries of Soft Defect Localization Tools

2021 ◽  
Author(s):  
Allan Norico ◽  
Rommel Estores
Keyword(s):  
Dynamic Analysis ◽  
Ambient Temperature ◽  
Failure Mode ◽  
Cold Temperature ◽  
Temperature Dependent ◽  
Dependent Failures ◽  
Defect Localization ◽  
External Conditions ◽  
Internal Signal ◽  
Serial Peripheral Interface

Abstract Temperature dependent failures are some of the most challenging cases that will be encountered by the analyst. Soft Defect Localization (SDL) is a technique used to analyze such temperature-dependent, ‘soft defect’ failures [1]. There are many literatures that discuss this technique and its different applications [2-7]. Dynamic Analysis by Laser Stimulation (DALS) is one of the known SDL implementations [8-11]. However, there are cases where the failure is occurring at a temperature where the laser alone is not sufficient to effectively induce a change of device behavior. In these situations, the analyst needs to think out of the box by understanding how the device will react to external conditions and to make necessary adjustments in DALS settings. This paper will discuss three cases that presents different challenges such as performing DALS analysis where the failing temperature is too high for the laser to induce a change of behavior from ambient temperature, cold temperature failure, complex triggering (Serial Peripheral Interface, SPI), and using an internal signal as input for DALS analysis. The approach used for a successful DALS analysis of each case will be discussed in detail.

Using NI-PXI Test Platform in Soft Defect Localization of Temperature Dependent Failures

2020 ◽  
Author(s):  
Jed Paolo Deligente ◽  
Saidaliah Sarip
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Failure Mode ◽  
Fault Isolation ◽  
Temperature Dependent ◽  
Labview Software ◽  
Dependent Failures ◽  
Analysis Technique ◽  
Test Platform ◽  
Defect Localization

Abstract Soft Defect Localization (SDL) method has been a common failure analysis technique used in fault isolation of temperature dependent failures, however proper signal conditioning and conversion of the monitored signal into a pass/fail signal are critical in acquiring an accurate defect location. This paper presents case studies where LabVIEW software using NI-PXI test platform was successfully implemented to effectively convert the failure mode into a pass/fail signal which provided a reliable SDL result.

Low Temperature Fault Isolation Using Soft Defect Localization

2012 ◽  
Author(s):  
Kristopher D. Staller
Keyword(s):  
Low Temperatures ◽  
Laser Scanning ◽  
Low Cost ◽  
Fault Isolation ◽  
Laser Scanning Microscopy ◽  
Cold Temperature ◽  
Multiple Point ◽  
Defect Localization ◽  
Low Levels

Abstract Cold temperature failures are often difficult to resolve, especially those at extreme low levels (< -40°C). Momentary application of chill spray can confirm the failure mode, but is impractical during photoemission microscopy (PEM), laser scanning microscopy (LSM), and multiple point microprobing. This paper will examine relatively low-cost cold temperature systems that can hold samples at steady state extreme low temperatures and describe a case study where a cold temperature stage was combined with LSM soft defect localization (SDL) to rapidly identify the cause of a complex cold temperature failure mechanism.

Ambient temperature-dependent thermoregulatory role of REM sleep

2012 ◽  
Vol 37 (5) ◽  
pp. 392-396
Author(s):  
Deependra Kumar ◽  
Velayudhan Mohan Kumar ◽  
Hruda Nanda Mallick
Keyword(s):  
Ambient Temperature ◽  
Rem Sleep ◽  
Temperature Dependent

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

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