Isamu Watanabe

Emergent media environment for idea creation support

Abstract An interactive computer system for supporting the emergent stages (or upper streams) in human intellectual activities has been developed. In the system it is intended to integrate facilities for supporting generation, collection, organisation, and presentation of ideas and advising on the divergence and convergence of the ideas. This paper explains the facilities of the system and how to use it, and describes some examples of their use.

Development and evaluation of human thinking support tools

Our efforts for developing and evaluating human thinking support tools are summarized. First, the organization and functions of the tools are described briefly, then three levels for evaluation are identified, and several methods in each level are explained. Results for applying the evaluation methods to the tools show the effectiveness of them.

Fault sound simulations from normal sounds for data-driven prognosis based on human expert and vibration knowledge

This paper discusses how to simulate plausible machine fault sound data from normal machine sounds using a combination of human empirical knowledge and past case studies of vibration monitoring. We focus on the fact that human experts in many maintenance areas generally listen to sounds when monitoring machine conditions. They can determine machine conditions by listening. Therefore, it is possible to assess machine fault sound simulators based on whether humans find the sound plausible. We successfully generated abnormal sounds that were synthesized from normal machine sounds using several parameters. The simulated sounds were assessed in two steps. One used theoretically synthesized sounds at various loudness levels and a vibration knowledge database. The other involved assessment by human experts by ear. We successfully generated plausible sounds for three rotor rotation machine failures and four roller bearing faults. The sounds were assessed by three human experts who had more than 10 years of experience in machine maintenance in two different areas. Their assessment results were consistent for our parameters.

Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets including the information of various states such as operation modes, but the fault dataset with the target variable is insufficient as the training dataset. This paper describes a semi-supervised approach to soft sensor modeling to incorporate an incomplete dataset without the target variable in the training dataset. To incorporate the incomplete dataset, we consider the properties of processes at transition points between operation modes in the system. The regression coefficients of the operation modes are estimated under constraint conditions obtained from the information on the mode transitions. In a case study, this constrained soft sensor modeling was used to predict refrigerant leaks in air-conditioning systems with heating and cooling operation modes. The results show that this modeling method is promising for soft sensors in a system with multiple operation modes.