{'fraction_non_alphanumeric': 0.028622540250447227, 'fraction_numerical': 0.0, 'mean_word_length': 4.957446808510638, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fault detection and isolation in wind turbines using support vector machines and observers

In this work, the benchmark FAST that simulates a closed-loop three-bladed wind turbine is used for fault detection and isolation. Two methods were employed to isolate faults of different types at different locations: Support vector machines (SVM) and a Kalman-like observer. SVM could isolate most faults with the used data and characteristic vectors, except for high varying dynamics. In this case, the use of an observer, which is model-based, was found necessary.