{'fraction_non_alphanumeric': 0.023454157782515993, 'fraction_numerical': 0.0, 'mean_word_length': 5.612676056338028, '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}}
PREDICTION FOR CONDITION MONITORING OF WEAR AND FILM THICKNESS IN A GEAR BOX

Gears are used to transmit power and motion in mechanical, electrical and chemical process industries. Influenced by vibration, torque, temperature, lubrication & specific film thickness, the gear teeth contacts may experience change leading to unexpected failures such as wear, scuffing, pitting and micro-pitting on teeth surface. In order to avoid these damages, continuous monitoring is essential using knowledge based systems. Generic capability of artificial neural network (ANN) is exploited to formulate prediction and classification based on heuristic models of condition of lubricating oil in spur gears. Based on the loading conditions such as vibration, temperature and torque, the algorithm predicts film thickness to classify oil conditions as elastohydrodynamic (EHD), mixed wear and severe wear that helps in detecting faults in gear operation.