{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detecting-spacecraft-anomalies-using-lstms","title":"Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding","arxiv_id":"1802.04431","date":"2018-02-13","proceeding":null,"authors":["Kyle Hundman","Valentino Constantinou","Christopher Laporte","Ian Colwell","Tom Soderstrom"],"abstract":"As spacecraft send back increasing amounts of telemetry data, improved\nanomaly detection systems are needed to lessen the monitoring burden placed on\noperations engineers and reduce operational risk. Current spacecraft monitoring\nsystems only target a subset of anomaly types and often require costly expert\nknowledge to develop and maintain due to challenges involving scale and\ncomplexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs)\nnetworks, a type of Recurrent Neural Network (RNN), in overcoming these issues\nusing expert-labeled telemetry anomaly data from the Soil Moisture Active\nPassive (SMAP) satellite and the Mars Science Laboratory (MSL) rover,\nCuriosity. We also propose a complementary unsupervised and nonparametric\nanomaly thresholding approach developed during a pilot implementation of an\nanomaly detection system for SMAP, and offer false positive mitigation\nstrategies along with other key improvements and lessons learned during\ndevelopment.","url_abs":"http://arxiv.org/abs/1802.04431v3","url_pdf":"http://arxiv.org/pdf/1802.04431v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/khundman/telemanom","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/D3-AI/Orion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/ML4ITS/mtad-gat-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/PKUZHOU/anomaly_det_cpp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/akshu281/KDD_LSTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/cc-pine/telemanom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/chunjingxiao/diffad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/datamllab/tods","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/signals-dev/Orion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"detecting-spacecraft-anomalies-using-lstms","repo_url":"https://github.com/xu737/PeFAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[{"slug":"msl","name":"MSL","full_name":"Mars Science Laboratory"},{"slug":"smap","name":"SMAP","full_name":"Soil Moisture Active Passive"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.04431","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}