{'fraction_non_alphanumeric': 0.014773776546629732, 'fraction_numerical': 0.0, 'mean_word_length': 5.376470588235295, '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}}
A Review of Fault Diagnosis Methods Based on Machine Learning Patterns

As one of the important methods in the field of artificial intelligence, machine learning plays a crucial role in the promoting engineering applications and the academic research. In recent years, with the rapid development of the field of artificial intelligence, other fields using artificial intelligence as a means has also made great breakthroughs, such as fault diagnosis. The traditional fault diagnosis method is based on a variety of different signal acquisition, signal processing, signal analysis means for equipment fault diagnosis and detection, while the fault diagnosis method based on machine learning has made a great breakthrough in recent years, and plays an important role in the field of fault diagnosis. This paper first describes the basic concepts of machine learning and fault diagnosis, and then describes several common machine learning methods, and summarizes and analyzes the development status in recent years. Finally, the author puts forward some of his own views and summarizes.