Fleet Prognosis with Physics-informed Recurrent Neural Networks

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer perceptron, the stress intensity and Paris law layers, the cumulative damage cell, as well as python driver scripts) used in this manuscript are publicly available on GitHub at https://github.com/PML-UCF/pinn. The data and code are released under the MIT Licens

Development Of An Optical Character Recognition Function System For Integrated Circuit Label Classification Using Neural Network

Presently, many Integrated Circuit (IC) manufacturers are applying machine vision solution to ensure the legibility of characters printed on the top surface of IC Package. In template matching technique there are about 10% of ICs rejected due to very little defects in quality of marking even though the characters are correct. The objective of this project is to develop an IC inspection system that has optical character recognition function system by using neural network. Feed forward back propagation neural network method is used in this task. The system developed is able to read 36 characters ( A to Z and 0 to 9) printed on ICs. The recognition time in template matching is 650μs. In neural network technique, by feeding-in Raw Data, Feature, and Hybrid (combination of Raw Data and Feature), they clock 18.22μs, 15.64μs and 19.32μs respectively. The recognition accuracy is 96.26% for the former and 98.25%, 98.83% and 99.61% for the latter. This is a solution to minimise rejects of ICs in manufacturing process. The reduction of processing time in manufacturing process contributes to the increase of productivity. Moreover, application of this technique gives a solution to avoid mismatch of parts (ICs) in manufacturing lots

Deep learning based vision inspection system for remanufacturing application

Deep Learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, localisation, natural language processing, prediction and forecasting systems. With significant applicability, Deep Learning is continually seeking other new fronts of applications for these techniques. This research is the first to apply Deep Learning algorithm to inspection in remanufacturing. Inspection is a key process in remanufacturing, which is currently an expensive manual operation in the remanufacturing process that depends on human operator expertise, in most cases. This research further proposes an automation framework based on Deep Learning algorithm for automating this inspection process. The proposed technique offers the potential to eliminate human factors in inspection, save cost, increase throughput and improve precision. This paper presents a novel vision-based inspection system on Deep Convolution Neural Network (DCNN) for three types of defects, namely pitting, surface abrasion and cracks by distinguishing between these surface defected parts. The materials used for this feasibility study were 100cm x 150cm mild steel plate material, purchased locally, and captured using a web webcam USB camera of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies, especially for accuracy and speed. This preliminary study demonstrates that Deep Learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of Deep Learning algorithms to remanufacturing

Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection

Industrial visual machine inspection system uses template or feature matching methods to locate or inspect parts or pattern on parts. These algorithms could not compensate for the change or variation on the inspected parts dynamically. Such problem was faced by a multinational semiconductor manufacturer. Therefore a study was conducted to introduce a new algorithm to inspect integrated circuit package markings. The main intend of the system was to verify if the marking can be read by humans. Algorithms that the current process uses however, was not capable in handling mark variations that was introduced by the marking process. A neural network based pattern recognition system was implemented and tested on images resembling the parts variations. Feature extraction was made simple by sectioning the region of interest (ROI) on the image into a specified (by the user) number of sections. The ratio of object pixels to the entire area of each section is calculated and used as an input into a feedforward neural network. Error-back propagation algorithm was used to train the network. The objective was to test the robustness of the network in handling pattern variations as well as the feasibility of implementing it on the production floor in tetms of execution speed. Two separate programme modules were written in C++; one for feature extraction and another for neural networks classifier. The feature extraction module was tested for its speed using various ROI sizes. The time taken for processing was round to be almost linearly related to the ROJ size and not at all effected by the number of sections. The minimum ROJ setting (200 X 200 pixels) was considerably slower at 5 5ms compared to what was required - 20ms. The neural networks c1assifier was very successful in classifying 1 3 different image patterns by learning from 4 training patterns. The classifier also clocked an average speed of 9.6ms which makes it feasible to implement it on the production floor. As a final say, it can be concluded that by carefully surveying the choices of hardware and software and its appropriate combination, this system can be seriously considered for implementation on the semiconductor production floor

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network