Information Extraction from Helicopter Maintenance Records as a Springboard for the Future of Maintenance Text Analysis

McKenzie, Amber; Matthews, Manton M.; Goodman, Nicholas; Bayoumi, Abdel

doi:10.1007/978-3-642-13022-9_59

Cited by 10 publications

(25 citation statements)

References 7 publications

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“…Maintenance text itself is often more similar to shorthand notation than the standard English text. 9,10 Stop words, commonly removed in NLP, provide important context for the interpretation of MWO. 14 As seen in Table 1, many of the words are domain-specific and most are abbreviations or acronyms, some created by specific individuals or groups of maintainers, 9,10,30 that are used inconsistently and interchangeably 14,31 and are not consistently marked with periods.…”

Section: Maintenance Text Challengesmentioning

confidence: 99%

“…3 This may not be an issue for academic or research NLP systems: they are often successful when trained on "standard" text that comes from, for example, English news wire and other literature. 8,9 However, text encountered in technical applications, such as in industrial operations, differs significantly from these benchmarks, causing performance of deployed NLP systems to drop, 8,10 often to unacceptable levels.…”

mentioning

confidence: 99%

“…For example, the lexical, grammatical, and terminological differences between "standard" English and that found in industrial maintenance logs have spawned a whole set of domain-specific NLP adaptations that are largely outside of mainstream NLP. 9,14 The classical NLP goal of having computers attain human-like language abilities 3 may also bias NLP toward impressive-but complex and resource-intensive-technologies, while ignoring those that are more in line with practical engineering needs. 15,16 With all this in mind, we sought an approach which will help bridge the gap between the promise of NLP and the realities confronted in many technical domains.…”

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confidence: 99%

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Adapting natural language processing for technical text

Dima

Lukens²,

Hodkiewicz

et al. 2021

Applied AI Letters

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Despite recent dramatic successes, natural language processing (NLP) is not ready to address a variety of real‐world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and large computational resource requirements, makes domain‐specific application of even the most successful NLP techniques difficult. This paper proposes technical language processing (TLP) which brings engineering principles and practices to NLP specifically for the purpose of extracting actionable information from language generated by experts in their technical tasks, systems, and processes. TLP envisages NLP as a socio‐technical system rather than as an algorithmic pipeline. We describe how the TLP approach to meaning and generalization differs from that of NLP, how data quantity and quality can be addressed in engineering technical domains, and the potential risks of not adapting NLP for technical use cases. Engineering problems can benefit immensely from the inclusion of knowledge from unstructured data, currently unavailable due to issues with out of the box NLP packages. We illustrate the TLP approach by focusing on maintenance in industrial organizations as a case‐study.

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Section: Maintenance Text Challengesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Adapting natural language processing for technical text

Dima

Lukens²,

Hodkiewicz

et al. 2021

Applied AI Letters

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show abstract

“…Data analysis method has been proposed to estimate required man-hours for certain types of maintenance tasks [11]. In the air safety report [12] and maintenance text message [13], a text mining technique has been devised to extract new information. In previous works [14,15], a text mining algorithm has been devised to classify maintenance reports.…”

Section: Introductionmentioning

confidence: 99%

New Application of Data Analysis Using Aircraft Fault Record Data

Lee

Shin

Tsourdos

et al. 2018

Journal of Aerospace Information Systems

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“…In reference [7], maintenance history data was used to predict required man-hours for maintenance task. Also, based on text mining approach, air safety report [8] and maintenance text message [9] were used to enrich information. In reference [10], maintenance report data was utilized to identify recurring faults.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of aircraft engine in FDR (flight data recorder) data

Lee

Shin

Tsourdos

et al. 2017

IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)

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This paper deals with detection of anomalous behaviour of aircraft engines in FDR (flight data recorder) data to improve airline maintenance operations. To this end, each FDR data that records different flight patterns is first sampled at a fixed time interval starting at the takeoff phase, in order to map each FDR data into comparable data space. Next, the parameters related to the aircraft engine are only selected from the sampled FDR data. In this analysis, the feature points are chosen as the mean value of each parameter within the sampling interval. For each FDR data, the feature vector is then formed by arranging all feature points. The proposed method compares the feature vectors of all FDR data and detects an FDR data in which the abnormal behaviour of the aircraft engine is recorded. The clustering algorithm called DBSCAN (density-based spatial clustering of applications with noise) is applied for this purpose. In this paper, the proposed method is tested using realistic FDR data provided by NASA's open database. The results indicate that the proposed method can be used to automatically identify an FDR data in which the abnormal behaviour of the aircraft engine is recorded from a large amount of FDR data. Accordingly, it can be utilized for a high-level diagnosis of engine failure in airline maintenance operations.

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Information Extraction from Helicopter Maintenance Records as a Springboard for the Future of Maintenance Text Analysis

Cited by 10 publications

References 7 publications

Adapting natural language processing for technical text

Adapting natural language processing for technical text

New Application of Data Analysis Using Aircraft Fault Record Data

Anomaly detection of aircraft engine in FDR (flight data recorder) data

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