@misc{AssafoLautschSuawaetal., author = {Assafo, Maryam and Lautsch, Martin and Suawa, Priscile Fogou and Jongmanns, Marcel and H{\"u}bner, Michael and Reichenbach, Marc and Brockmann, Carsten and Reinhardt, Denis and Langend{\"o}rfer, Peter}, title = {The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-6204-0}, pages = {S. 541}, language = {en} } @misc{AssafoLangendoerfer, author = {Assafo, Maryam and Langend{\"o}rfer, Peter}, title = {Unsupervised and semisupervised machine learning frameworks for multiclass tool wear recognition}, series = {IEEE Open Journal of the Industrial Electronics Society}, volume = {5 (2024)}, journal = {IEEE Open Journal of the Industrial Electronics Society}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2644-1284}, doi = {10.1109/OJIES.2024.3455264}, pages = {993 -- 1010}, language = {en} } @misc{AssafoLangendoerfer, author = {Assafo, Maryam and Langend{\"o}rfer, Peter}, title = {A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools under Different Operating Conditions}, series = {IEEE Access}, journal = {IEEE Access}, number = {9}, issn = {2169-3536}, doi = {10.1109/ACCESS.2021.3091476}, pages = {90011 -- 90028}, language = {en} } @misc{AssafoStaedterMeiseletal., author = {Assafo, Maryam and St{\"a}dter, Jost Philipp and Meisel, Tenia and Langend{\"o}rfer, Peter}, title = {On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s23094461}, abstract = {Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA's repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR's stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble.}, language = {en} } @misc{AssafoLangendoerfer, author = {Assafo, Maryam and Langendoerfer, Peter}, title = {Tool remaining useful life prediction using feature extraction and machine learning-based sensor fusion}, series = {Results in engineering}, volume = {28}, journal = {Results in engineering}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2590-1230}, doi = {10.1016/j.rineng.2025.107297}, pages = {1 -- 14}, abstract = {Tool remaining useful life prediction (RUL) is a critical task for predictive maintenance in manufacturing. Common limitations of existing data-driven solutions include: 1) Dependence on tool wear labels which are intricate to obtain on shop floors. 2) High resource requirements, affecting applicability on resource-constrained Internet-of-things devices. 3) Heavy feature engineering. To address these limitations, we present a methodology aiming at accurately predicting RUL without using wear labels, while ensuring implementation efficiency and minimal feature engineering. It involves extracting time-domain features and multiscale features using maximal overlap discrete wavelet transform (MODWT) from three cutting-force sensor signals. Without undergoing any feature selection or dimensionality reduction, the features are fed to machine learning (ML) regression models where they are fused into an RUL decision. For this purpose, one-to-one and sequence-to-sequence regression using random forest (RF) and different long short-term memory (LSTM) networks were used, respectively. The 2010 PHM Data Challenge milling dataset was used for validation. The results highlighted the significant role of sensor fusion in reducing prediction errors and increasing the performance consistency over three test cutters, compared to single sensors. Global interpretations were provided using RF-based feature importance analysis. Our methodology was compared with six existing state-of-the-art works, including different end-to-end deep learning (DL) models using raw data as input, and works coupling heavy feature engineering with DL. The results showed that our methodology consistently outperformed all the comparative methods over the test cutters, despite using fewer sensors, which further proves its competitiveness and suitability in resource- and sensor-constrained environments.}, language = {en} } @misc{AssafoLangendoerfer, author = {Assafo, Maryam and Langendoerfer, Peter}, title = {Self-organizing map applications for predictive maintenance : a review}, series = {2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)}, journal = {2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, isbn = {979-8-3315-4299-3}, doi = {10.1109/ICPS65515.2025.11087894}, pages = {1 -- 7}, abstract = {Machine learning (ML) has proven to be a key enabler of various industrial cyber-physical systems-empowered functionalities, such as predictive maintenance (PdM) in Industry 4.0. Among many ML methods, the scope of this paper is self-organizing map (SOM). SOM has many attractive properties for industry applications (e.g., unsupervised learning, noise robustness). Further, it can yield various tasks (e.g., clustering, dimensionality reduction, health indicator construction, visualization, etc.), rendering it versatile for data-driven PdM, including anomaly detection, fault diagnosis, and prognosis tasks. This paper presents a brief review of the main SOM applications in the PdM field, as well as future research opportunities.}, language = {en} }