# Detecting Aircraft Engine Anomalies with Autoencoders: Honeywell Patent Analysis An autoencoder is a neural network trained through unsupervised learning to encode data and reconstruct an output as close as possible to the input. Source: https://www.iplexlaw.co.kr/en/blog/881215 HOME / NEWS & INSIGHTS NEWS & INSIGHTS Detecting Aircraft Engine Anomalies with Autoencoders: Honeywell Patent Analysis An autoencoder is a neural network trained through unsupervised learning to encode data and reconstruct an output as close as possible to the input. AI & Software 2023.07.07 published IPLEX 4 min read An autoencoder is a neural network trained through unsupervised learning to encode data and reconstruct an output as close as possible to the input. An autoencoder aims to reconstruct inputs similar to the data it encountered during training; unfamiliar inputs may produce larger reconstruction errors. This is why autoencoders are widely used for anomaly detection. This article examines a Honeywell patent on detecting engine faults using autoencoders. If you click on the image, you can see the patent. Claim 1 1. A fault detection system for detecting faults in a turbine engine, the fault detection system comprising: an encoding neural network, the encoding neural network receiving sensor data from the turbine engine, the encoding neural network creating plurality of scores from the sensor data, the plurality of scores comprising a reduced feature space representation of the sensor data; wherein the encoding network is trained with an objective function using historical sensor data, where the objective function includes a sum of variance components and a sum of covariance components, and wherein the encoding neural network is trained with the objective function by maximizing the sum of variance components and minimizing the sum of covariance components; and a decoding neural network, the decoding neural network receiving the plurality of scores and creating a reconstructed estimate of the sensor data. Traditional engine error detection systems performed error detection based on the linear relationship between the variables of the system, so they were not effective in detecting errors in nonlinear systems. In addition, the sensor data of the turbine engine of the aircraft had a nonlinear relationship, making it difficult to detect the failure of the turbine engine of the aircraft with traditional engine fault detection systems. The present invention aims to solve these problems. Claim 1 describes an encoder that maps turbine-engine sensor data into scores representing a lower-dimensional feature space. A decoder reconstructs an estimate of the sensor data from those scores. Together, they form an autoencoder architecture. The encoder is trained on historical sensor data using an objective that maximizes the sum of variance components and minimizes the sum of covariance components. Faults are detected using differences between the input sensor data and its reconstruction, a common approach to anomaly detection with autoencoders. The claim is limited to detecting turbine-engine faults, which defines its field of application. A claim without that domain limitation could potentially be broader, but its support, novelty and inventive step would still need to be assessed. In case of patent application related to AI technology, it is quite important to write a patent claim so that it has the widest range of rights possible. Read the Korean source This article reflects the information available when it was published. Contact us to discuss your circumstances. 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