Predicting equipment anomalies before they escalate into failures is a critical challenge in industrial facility management. Existing approaches rely either on hand-crafted threshold rules, which lack generalizability, or on large neural models that are impractical for on-site, air-gapped deployments. We present an industrial methodology that resolves this tension by combining open-source small foundation models into a unified 1,116-dimensional Triplet Feature Fusion pipeline. This pipeline integrates: (1) statistical features (x in $R^{28}$) derived from 90-day sensor histories, (2) time-series embeddings (y in $R^{64}$) from a LoRA-adapted IBM Granite TinyTimeMixer (TTM, 133K parameters), and (3) multilingual text embeddings (z in $R^{1024}$) extracted from Japanese equipment master records via multilingual-e5-large. The concatenated triplet h = [x; y; z] is processed by a LightGBM classifier (< 3 MB) trained to predict anomalies at 30-, 60-, and 90-day horizons. All components use permissive open-source licenses (Apache 2.0 / MIT). The inference-time pipeline runs entirely on CPU in under 2 ms, enabling edge deployment on co-located hardware without cloud dependency. On a dataset of 64 HVAC units comprising 67,045 samples, the triplet model achieves Precision = 0.992, F1 = 0.958, and ROC-AUC = 0.998 at the 30-day horizon. Crucially, it reduces the False Positive Rate from 0.6 percent (baseline) to 0.1 percent - an 83 percent reduction attributable to equipment-type conditioning via text embedding z. Cluster analysis reveals that the embeddings align time-series signatures with distinct fault archetypes, explaining how compact multilingual representations improve discrimination without explicit categorical encoding.
Minimal-Configuration Anomaly Detection for IIoT Sensors
Center for Digital Safety and Security Austrian Institute of Technology（奥地利科技学院数字安全与保障中心）；Distributed AI Systems Research Group Siemens Technology Austria（西门子奥地利技术公司分布式人工智能系统研究小组）
A task of anomaly detection for a smart satellite Internet of things system
School of Mathematics, Hefei University of Technology（合肥工业大学数学学院）；School of Electrical and Automation Engineering, Hefei University of Technology（合肥工业大学电气与自动化工程学院）；CCNU Wollongong Joint Institute, Central China Normal University（华中师范大学伍伦贡联合研究院）
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究院）；Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada（格拉纳达大学计算机科学与人工智能系）
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Cifrum – Nuclear Industry Digitalization, Private Enterprise（私营企业Cifrum——核工业数字化公司）；Digital Technologies and Platforms, LLC（数字技术与平台有限责任公司）
Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping
SENAI Institute of Innovation for Information and Communication Technologies (ISI-TICs)（SENAI信息与通信技术创新研究所（ISI-TICs））；Federal Rural University of Pernambuco (UFRPE)（伯南布哥联邦农村大学（UFRPE））
PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection
School of Mathematical Sciences, Guizhou Normal University（贵州师范大学数学科学学院）；School of Big Data and Computer Science, Guizhou Normal University（贵州师范大学大数据与计算机科学学院）
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
Dept. of Computing Sciences Tampere University（计算科学系塔尔皮大学）；Dept. of Information Engineering University of Padova（信息工程系帕多瓦大学）；Dept. of Electrical and Electronics Engineering Izmir University of Economics（电气与电子工程系伊兹密尔经济大学）；Dept. of Media Engineering and Technology German International University（媒体工程与技术系德国国际大学）
Few-Shot Network Intrusion Detection Using Online Triplet Mining
Department of Electronics and Electrical Engineering, University of Strathclyde（斯特拉斯克莱德大学电子与电气工程系）；Faculty of Computer and Information Sciences, Ain Shams University（爱思曼大学计算机与信息科学学院）；Faculty of Electrical Engineering, Czech Technical University（捷克技术大学电气工程学院）
Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI
Electrical and Computer Engineering Department, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Natural Sciences, Pasadena City College（帕萨迪纳城市学院自然科学系）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Department of Data Science and AI, Monash University（数据科学与人工智能系，墨尔本大学）；Department of Computer Science, University of Oxford（计算机科学系，牛津大学）；IBM T. J. Watson Research Center（IBM T. J. Watson研究中心）；Squirrel Ai Learning（Squirrel Ai Learning公司）；School of ICT, Griffith University（信息科技学院，格里菲斯大学）
