Change‐Point Detection on Solar Panel Performance Using Thresholded LASSO

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135028/1/qre2077.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135028/2/qre2077_am.pd

Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, a diagnostic system can be developed and its performance is continuously improved as the knowledge of process faults is automatically accumulated during production. As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46524/1/10845_2004_Article_337289.pd

Online Eccentricity Monitoring of Seamless Tubes in Cross-Roll Piercing Mill

Wall-thickness eccentricity is a major dimensional deviation problem in seamless steel tube production. Although eccentricity is mainly caused by abnormal process conditions in the cross-roll piercing mill, most seamless tube plants lack the monitoring at the hot piercing stage but only inspect the quality of finished tubes using ultrasonic testing (UT) at the end of all manufacturing processes. This paper develops an online monitoring technique to detect abnormal conditions in the cross-roll piercing mill. Based on an imagesensing technique, process operation condition can be extracted from the vibration signals. Optimal frequency features that are sensitive to tube wall-thickness variation are then selected through the formulation and solution of a set-covering optimization problem. Hotelling T 2 control charts are constructed using the selected features for online monitoring. The developed monitoring technique enables early detection of eccentricity problems at the hot piercing stage, which can facilitate timely adjustment and defect prevention. The monitoring technique developed in this paper is generic and can be widely applied to the hot piercing process of various products. This paper also provides a general framework for effectively analyzing image-based sensing data and establishing the linkage between product quality information and process information

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant

Keeping Ground Robots on the Move Through Battery and Mission Management

U.S. Army TARDEChttp://deepblue.lib.umich.edu/bitstream/2027.42/110690/1/0614MEM_DSC_ErsalFINAL.pdfDescription of 0614MEM_DSC_ErsalFINAL.pdf : Main article page proof