# US 2021/0279596 A1 · System for predictive maintenance using trace norm generative adversarial networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279596 A1 - Document ID: US20210279596A1 - Status: Published application - Kind: A1 - Application number: 16/812,088 - Filed: 2020-03-06 - Published: 2021-09-09 - Earliest claimed: 2020-03-06 - Assignees: Hitachi, Ltd. - Applicants: Hitachi, Ltd. - Inventors: Shuai ZHENG; Chetan GUPTA - Web page: https://patentgrep.com/patent/US20210279596A1 ## CPC - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/094: Adversarial learning - G06Q 10/20: Administration of product repair or maintenance - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Example implementations involve a system for a system and method for Predictive Maintenance using Trace Norm Generative Adversarial Networks. Such example implementations can involve providing generated sensor data and real sensor data to a first network and to a second network, the first network configured to enforce trace norm minimization of the second network, the second network configured to distinguish between the generated sensor data and the real sensor data, the first network involving a subset of layers from the second network, and the second network integrated into a generative adversarial network. ## Claims ### Claim 1 A method for training and deploying a failure prediction model, the method comprising: providing generated sensor data and real sensor data to a first network and to a second network, the first network configured to enforce trace norm minimization of the second network, the second network configured to distinguish between the generated sensor data and the real sensor data, the first network comprising a subset of layers from the second network, the real sensor data comprising pairs of real sensor data and labels, the second network integrated into a generative adversarial network (GAN); training the failure prediction model from the output of the first network using the provided generated sensor data and the real sensor data, the output of the first network comprising feature vectors; and deploying the failure prediction model with the first network, the deployed first network configured to intake the real sensor data to output the feature vectors to the failure prediction model. ### Claim 2 (depends on 1) The method of claim 1, wherein training the failure prediction model from the output of the first network comprises iteratively updating the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and the second network converges; and wherein the instructions further comprise providing neural network model parameters of the first network and the second network for the failure prediction model. ### Claim 3 (depends on 1) The method of claim 1, wherein the providing generated sensor data comprises providing an input noise vector into a third network configured to provide the generated sensor data; wherein the training the failure prediction model from the output comprises iteratively updating the third network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and the second network converges. ### Claim 4 (depends on 1) The method of claim 1, wherein the second network is integrated with another network configured to maximize mutual information between latent code used to generate the generated sensor data and the generated sensor data; wherein the training the failure prediction model from the output comprises iteratively updating the another network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and the second network converges. ### Claim 5 (depends on 1) The method of claim 1, wherein the first network is integrated into an infoGAN. ### Claim 6 (depends on 1) The method of claim 1, wherein the failure prediction model is configured to output a label for the sensor data as either failure or non-failure. ### Claim 7 A non-transitory computer readable medium storing instructions for training and deploying a failure prediction model, the instructions comprising: providing generated sensor data and real sensor data to a first network and to a second network, the first network configured to enforce trace norm minimization of the second network, the second network configured to distinguish between the generated sensor data and the real sensor data, the first network comprising a subset of layers from the second network, the real sensor data comprising pairs of real sensor data and labels, the second network integrated into a generative adversarial network (GAN); training the failure prediction model from the output of the first network using the provided generated sensor data and the real sensor data, the output of the first network comprising feature vectors; and deploying the failure prediction model with the first network, the deployed first network configured to intake the real sensor data to output the feature vectors to the failure prediction model. ### Claim 8 (depends on 7) The non-transitory computer readable medium of claim 7, wherein training the failure prediction model from the output of the first network comprises iteratively updating the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and second network converges; and wherein the method further comprises providing neural network model parameters of the first network and the second network for the failure prediction model. ### Claim 9 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the providing generated sensor data comprises providing an input noise vector into a third network configured to provide the generated sensor data; wherein the training the failure prediction model from the output comprises iteratively updating the third network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and second network converges. ### Claim 10 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the second network is integrated with another network configured to maximize mutual information between latent code used to generate the generated sensor data and the generated sensor data; wherein the training the failure prediction model from the output comprises iteratively updating the another network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and second network converges. ### Claim 11 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the first network is integrated into an infoGAN. ### Claim 12 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the failure prediction model is configured to output a label for sensor data as either failure or non-failure. ### Claim 13 An apparatus configured for training and deploying a failure prediction model, the apparatus comprising: a processor, configured to: provide generated sensor data and real sensor data to a first network and to a second network, the first network configured to enforce trace norm minimization of the second network, the second network configured to distinguish between the generated sensor data and the real sensor data, the first network comprising a subset of layers from the second network, the real sensor data comprising pairs of real sensor data and labels, the second network integrated into a generative adversarial network (GAN); train the failure prediction model from the output of the first network using the provided generated sensor data and the real sensor data, the output of the first network comprising feature vectors; and deploy the failure prediction model with the first network, the deployed first network configured to intake the real sensor data to output the feature vectors to the failure prediction model. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the processor is configured to train the failure prediction model from the output of the first network by iteratively updating the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and second network converges; and wherein the processor is further configured to provide neural network model parameters of the first network and the second network for the failure prediction model. ### Claim 15 (depends on 13) The apparatus of claim 13, wherein the processor is configured to provide generated sensor data by providing an input noise vector into a third network configured to provide the generated sensor data; wherein the processor is configured to train the failure prediction model from the output by iteratively updating the third network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and the second network converges. ### Claim 16 (depends on 13) The apparatus of claim 13, wherein the second network is integrated with another network configured to maximize mutual information between latent code used to generate the generated sensor data and the generated sensor data; wherein the processor is configured to train the failure prediction model from the output by iteratively updating the another network with the first network and the second network based on loss between the generated sensor data and the real sensor data as determined for each of the first network and the second network until the loss of the first network and second network converges. ### Claim 17 (depends on 13) The apparatus of claim 13, wherein the first network is integrated into an infoGAN. ### Claim 18 (depends on 13) The apparatus of claim 13, wherein the failure prediction model is configured to output a label for sensor data as either failure or non-failure.