{'fraction_non_alphanumeric': 0.017964071856287425, 'fraction_numerical': 0.0, 'mean_word_length': 5.458937198067633, '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 graphic pattern feature-mapping-based data-driven condition monitoring method for diesel engine malfunction identification and classification

Effective condition monitoring of diesel engine can ensure the reliability of large-power machines and prevent catastrophic consequences. Cylinder pressure is capable of reflecting the whole combustion process of diesel engine, and hence can help to identify the malfunctions of the diesel engine during operation. In this paper, a graphic pattern feature-mapping method is proposed for graphic pattern feature recognition in data-driven condition monitoring. The graphic feature extraction and recognition are linked by labeled feature-mapping. It is used for identifying the running condition of the diesel engine via analyzing the cylinder pressure signal of the diesel engine. The different types of the malfunctions which are caused by different parts of the diesel engine such as induction system, valve actuating mechanism, fuel system, fuel injection system, etc. can be identified just by cylinder pressure signal. The bench experiment of a large-power diesel engine is performed to validate this graphic pattern recognition method. The results show that it has good accuracy on multi-malfunction identification and classification when the engine operates at one speed and one load.