Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
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Creating New Value using Mass Text Data
Text Mining for Performance Improvement

KAZUKI OZAKI*1

SHUN YAMAGATA*2HISASHI NISHIKI*2

TAKASHI TAGUCHI*3

Text data, such as incidents and their causes relating to incompatibilities that occur during
manufacturing, troubleshooting during facility operation, patent information, etc., is stored ingeneral as unstructured data, unlike numerical data which expresses physical quantities, etc., intable form that can be structured*. Therefore, the development of text data analysis has been quite
slow, hindering its utilization. If such text data could be quantified and structured, classifying
sentences and searching for similar ones would be possible by utilizing an analytical approach to
numerical data, from which the effective use of text data such as a quick response to newly
occurring incidents could be expected. This paper introduces text mining technology andapplication examples where text data could be quantified and structured.
*Structuring is facilitating the handling of large-volume, complex incidents by sorting them into smallercategories in phases.

|1. Introduction
Mitsubishi Heavy Industries, Ltd. (MHI) has been working on predicting future energy
demand utilizing data analysis techniques as one of our solutions to respond to the changes in theenergy environment that have become increasingly significant due to global environmental issues
and power deregulation, and to achieve more efficient energy management. The data subject toanalysis here consists of various types of numerical data such as physical quantities measured byvarious sensors attached to factory equipment, calendar information, weather information and time
information linking them together, which is structured data expressed in table form.
Meanwhile, unstructured data including text data is generally thought to account for 80% of
all data owned by companies. MHI also owns a large quantity of text data, including measuresagainst incompatibility incidents occurring at the time of manufacture, expertise for efficientfacility operation, patent documents, etc., which is unstructured and has seen very little progress interms of analysis such as evaluating text correlation in a quantitative manner, where its utilizationis still a long way off.
MHI successfully quantified and structured such unstructured text data by applying the text
mining technology described below where we have achieved clustering (classification) of anenormous number of sentences and searches of similar sentences, in an attempt to extract andutilize useful information from the text data we have collected. This attempt has allowed us tocreate measures against incompatibility incidents, to offer troubleshooting during facility operationand to achieve increased efficiency in document checking.

|2. Analytical procedure utilizing text mining technology
We are going to introduce in 4 steps a method of converting mass text data to numerical data,structuring a huge volume of unstructured text data and performing clustering (classification) and*1

EPI Department, ICT Solution Headquarters*2

Program Execution Manager, Power & Energy Solution Business Planning Department, Power & Energy Solution Business*3

Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems

Division, Power Systems

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
2searching, utilizing text mining technology. This method is widely used and is applied to thedemonstration of text mining described in the next chapter.
Step 1: Quantifying text data
Divide sentences into words by morphological analysis where text data is converted into
numerical data by calculating the number of occurrences of each word. Expression in table form
comprising the number of occurrences of sentences and words facilitates processing by computers(Figure 1).

Figure 1

Quantifying text data

Step 2: Extracting the potential semantic information from text data
Apply mathematical manipulation (probability/statistical manipulation) to a matrix of thewords described above and the number of occurrences, to build a conversion model where asentence can be expressed with the potential semantic information (topic) comprising multiple
groups of words and their occurrence probability rather than a matrix of words and frequencies(Figure 2) .

Figure 2

Extracting potential semantic information

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)3

Step 3: Clustering (classifying) sentences
Perform sentence classification based on hierarchical clustering utilizing inter-vector distancein terms of the occurrence probability vector of the topic obtained for each sentence in Step 2(Figure 3).
Step 4: Searching for sentences
Perform Steps 1 and 2 on any given sentence, whereby the occurrence probability vector of
the topic is calculated in the same manner as it would be for a sentence to be retrieved, and search
for/extract similar sentences according to the degree of similarity calculated from a comparison
with the occurrence probability vector of a sentence group to be retrieved (Figure 4).

Figure 3

Hierarchical clustering

Figure 4 Calculating the degree of similarity to
the sentence entered|3. Embodiments of text mining
This chapter will introduce 3 actual applications of text mining.
(1) Application to category presentation in incompatibility information management
Our Quality Assurance Department manages past incompatibility information using asystem that registers, at the time of incompatibility, the details, causes, countermeasures, etc.,
while working on recurrence prevention activities utilizing such information. Such activities arecrucial as they are closely related not only to product quality, but also to cost, delivery, safety
and customer satisfaction.
Incompatibility information is entered into the system by individual departments, but forvarious reasons, such as how to describe what happened not being standardized and varying
widely depending on the person, the information is not necessarily organized sufficiently to beutilized easily for recurrence prevention activities.
Accordingly, MHI has created a model that automatically performs the clustering ofvarious similar expressions in text by applying the text mining technique to past incompatibility
information where the individual clusters are automatically classified into multipleincompatibility reasons which are linked to respective countermeasures. Utilization of thismodel has allowed us to easily classify the expressions in text which have been quite random
depending on who wrote them, increasing the quality of recurrence prevention activities.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
4(2) Application to troubleshooting in large-scale facility test run
When problems occur in the operation of large equipment/plants, especially in a test run
at the time of start-up where troubleshooting takes time, losses such as delays in the start ofoperation, etc., are huge. Meanwhile, our Test Operation Department implements Fault Tree
Analysis (FTA) of problems that have occurred in the past, and compiled the details, causes,
measures, etc., into "Combat Lessons" which are utilized in troubleshooting and training youngengineers.
However, since the details of problems are diverse and consist of text data including
complicated technical terms, the conventional classification method would require theexperience and expertise of veteran engineers for selection from the Combat Lessons suitable
for the problems currently occurring, which is time-consuming.
Accordingly, the details of the problems, their causes and countermeasures compiled inpast "Combat Lessons" were first broken down into smaller parts and then made into a database
where they were subsequently classified into multiple phases applying the text mining
technique (Steps 1 to 3 in Chapter 2) according to the level of relevance of each type ofinformation. As a result, when a similar incident occurs, by entering text, for example, "Y ofMachine X stopped at the signal of Z," similarities to past incidents will be immediately
identified in the method of Step 4 in Chapter 2, allowing swift searches for an appropriate
response/treatment. Conventionally, implementing FTA itself used to require advanced
knowledge and experience. However, hierarchical classification using the text mining technique
now allows us to effectively utilize a large volume of records in text format.
(3) Application to patent search support in Intellectual Property Department
In patent searches, which are closely related to technical trends, etc., the scope ofsearches for patents owned by other companies used to be narrowed down by the patentclassification code given to each technical field and keywords related to products andequipment in the field of invention. However, when searching for new technologies where it isdifficult to select an appropriate patent classification code for abstract keywords like "AI&IoT,"
it is impossible to narrow the search in an effective manner, and the scope of search becomes
too extensive.
Accordingly, to efficiently narrow down the scope of search, the text mining technique isutilized to automatically classify abstract keywords within the search scope into relevant
technical fields, which allows us to increase efficiency in later phases in the process including
detailed search by experts in individual technical fields and to boost the accuracy of the search.

|4. Conclusion
This paper introduced a method of performing clustering (classification) and searches byutilizing text mining technology, where the quantification and structuring of mass text data isachieved, as well as the positive outcomes of the application of this method to text data such as
incompatibility information, Combat Lessons and patents, based on actual examples.
By establishing a general-purpose analysis method for text data, the types of data available
other than numerical data increases, which indicates the potential for offering new solutions thatutilize classification/search in addition to prediction to achieve efficient facility operation andimproved lead time in the work process.
In the future, MHI will strive to acquire new knowledge by integrating auxiliary information
to text data such as time of occurrence and the enormous amount of numerical data accumulating in
day-to-day operations, as well as to expand the range of solutions we offer.