Machine Learning Approach to Corrosion Maintenance Identification

Underreporting fleet corrosion maintenance records results in the data consumers, analyst, and decision makers with inaccurate information to pursue corrosion mitigations in order to maintain equipment mission readiness. The NAVAIR methodology of recording equipment maintenance entails completing a Visual Information Display System/Maintenance Action Form (VIDS/MAF). The VIDS/MAF includes underutilized fleet narratives that describe the equipment discrepancy and the corrective maintenance action. This research investigates whether an unsupervised machine-learning algorithm can process fleet text narratives and identify the corrosion maintenance records.

The research proceeded by employing Natural Language Processing (NLP) to the VIDS/MAF unstructured text data. The researchers acquired data comprising of a single piece of NAVAIR equipment that contains a generous quantity of both corrosion and non-corrosion related maintenance records. The unstructured maintenance text was preprocessed utilizing lemmatization, special character removal, lowercasing characters, removing punctuations and numbers, and stripping white space. The researchers applied a Latent Dirichlet Allocation (LDA) topic algorithm to the processed unstructured text iterated across a sequence of topic quantity hyper-parameters. A harmonic mean function identified the optimal number of hyper-parameter topics. The researchers applied the LDA algorithm with the optimal number of hyper-parameter topics against the dataset. This LDA model identified additional potential corrosion maintenance records by a factor of five.

The objective of this project consisted of determining whether NLP techniques can identify corrosion maintenance records beyond the capability of the structured fields found in the VIDS/MAF. The research was successful in identifying additional probable corrosion maintenance records. Furthermore, this research has the potential for utilization in analogous applications, such as identifying underreported wiring issues that is applicable across the Navy enterprise.


Machine Learning Approach to Corrosion Maintenance Identification

A common focus across the DoD is: How can our tools and information help improve fleet readiness? It is evident that Data Science plays an immense role when it comes to decision making and the analysis of issues. The increase of data digitization only furthers these efforts. One avenue of interest is Natural Language Processing (NLP), which is a subset of Artificial Intelligence (AI). NLP focuses on developing models to understand human language. This work focuses on a specific area of NLP called Word Embeddings, which maps words to vectors of real numbers. These numerical representations of words helps enhance modeling tasks such as multiclass classification. Our use-case demonstrates how Word Embeddings were able to enhance classification results of maintenance narratives.

The maintenance narratives are free-form text that the sailors input when completing maintenance actions. The dataset of about 60,000 records is labeled and has six different categories of maintenance. The process is then broken down into two approaches, the classical approach and then the word embedding approach. The classical approach involves the textual data being broken down into tokens and forming a Document Frequency Matrix (DFM). This DFM is then used to build supervised classifiers such as Naive Bayes and Gradient Boosting Machines. This is then compared to the Word Embedding approach, which also tokenizes the textual data, but then uses it to form word vector representations of each token. These vectors are then used to build a supervised classifier that yields better results than the classical approach.

Word Embedding models allows for a deeper understanding of human language as opposed to classical approaches. It is able to absorb more context and syntactic similarity from the text. The corpora of data that is used to train it determines how the language model will behave. In our case, the model is centric to naval maintenance data. However, such work could be applied in various fields where multiclass classification would be beneficial.


John Alexiades

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