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The purpose of this paper is to describe some risks and risk-taking when launching and running business in Russia during the socio-economic transformation. The risks are related to informal economy.

The research material consists of 11 thematic interviews of families, who launched and ran a business during the turn of the millennium and follow-up interviews. The interviews were extracted from a larger material of 100 interviews and 40 follow-ups focusing on household coping in Russian socio-economic transformation.

The paper focusses on risky shuttle trade as well as crime-related risks of business, as poignant illustrations of problems of entrepreneurship linked with informal economy. The risks are seen as social facts, objective or constructed, in some cases pointing out also how people speak about them. The testimonial stories of business show an individual or family focus on the risks, without major concern for legitimacy of action, albeit a need for distinction toward other corresponding risk-takers.

The findings illustrate important aspects of informal economy in Russia, but they cannot be generalized. It would be most useful to carry out qualitative studies of small business to assess perception and construction of risks and coping with them, capturing the views of entrepreneurs themselves.

There is rather little micro-level information concerning the risks or uncertainty when launching and running business in the Russian Federation. Qualitative data enable deeper understanding of sensitive issues. The paper wants to introduce the concept of risk into the social research of informal economy.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.