{'fraction_non_alphanumeric': 0.03205128205128205, 'fraction_numerical': 0.004006410256410256, 'mean_word_length': 5.152709359605911, '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}}
Enhancing a Fuzzy Failure Mode and Effect Analysis Methodology with an Analogical Reasoning Technique

In this paper, a fuzzy Failure Mode and Effect Analysis (FMEA) methodology incorporating an analogical reasoning technique is presented. FMEA methodology was introduced as a formal and systematic procedure for evaluation of risk associated with potential failure modes in the 1960s. Bowles and Pel´aez [1] proposed a Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model as an alternative to the conventional RPN model. For an FIS-based RPN (a three-input FIS model), a large set of fuzzy rules are required, and it is tedious to collect the full set of rules. With the grid partition strategy, the number of fuzzy rules required increases in an exponential manner, and this phenomenon is known as the “curse of dimensionality” or the combinatorial rule explosion problem. Hence, a rule selection and similarity reasoning technique, i.e., Approximate Analogical Reasoning Schema (AARS) technique are implemented in a fuzzy FMEA in order to solve the problem. The experiment was conducted using a set of data collected from a semiconductor manufacturing line, i.e., underfill dispensing process, and promising results were obtained.