Vol. 77, No. 1, January 2022

Special Reports

Next-Generation Measurement and Control Systems Supporting Digital Transformation

OKANIWA Fumihiko

ANAN Kazuhiro / SATO Mitsue / TAKAYANAGI Yoichi

A recent trend in measurement and control systems in the social infrastructure industrial field has been an acceleration in the transition from the Internet of Things (IoT) to digital transformation (DX). A wide variety of data collected by IoT devices via the network has begun to be effectively utilized through the application of cloud computing technologies. However, several issues related to DX exist in terms of the difficulty of integrating remote control and operation due to the coexistence of legacy and new equipment as well as security risks including cyberattacks and information leakages.

To rectify this situation, the Toshiba Group has embarked on improvement activities aimed at realizing measurement and control systems adapted to edge computing through the development of technologies including add-on control techniques and security technologies for control systems. From the perspective of both on-site DX and the remote control and operation of factories, we are accelerating efforts to develop next-generation measurement and control systems for further automation and labor saving through the collaboration of our accumulated edge computing technologies and cloud computing.

NIINUMA Yoshiki / INARI Masaru / NAKAMURA Takaki

In the social infrastructure and industrial fields, monitoring and control systems and automation systems are now confronting the issue of avoiding the excess concentration of loads on cloud systems as a consequence of the progress of digital transformation (DX). Demand has therefore been increasing for edge computing using distributed industrial servers in order to make effective use of the huge volumes of data collected from field devices.

Toshiba Infrastructure Systems & Solutions Corporation has now developed the FS20000R model 200/100 industrial servers satisfying the various needs of edge computing. These models offer the following features: (1) proper functions and specifications required in industrial settings, including long-term supply and support, reliability, and robustness; (2) a large storage capacity required for the growing volume of data from edge devices; (3) a high-performance central processing unit (CPU) architecture that achieves a processing speed approximately 1.3 times that of our conventional products; and (4) a scalable structure capable of implementing a graphics processing unit (GPU) board that realizes high-speed parallel processing for the application of artificial intelligence (AI) technologies.

MURAKAMI Keisuke / TATENO Genki / LIU Liu

The movement toward the realization of smart factories has recently been progressing in manufacturing industries. In particular, control systems continue to evolve for digital transformation (DX) through the utilization of data processed by edge and cloud computing.

Toshiba Infrastructure Systems & Solutions Corporation has already developed and released the Unified Controller Vm series typeS industrial controllers, which are playing a key role in the DX of conventional control systems. We are making continuous efforts to further enhance the functions and performance of such systems. In order to provide users with new cloud services focusing on DX, we have now developed nV-Tools Cloud, an integrated engineering environment that makes it possible to improve the efficiency of development and operation by means of remote engineering. In addition, we have developed a platform that allows individual users to manage and make effective use of information and data collected by the equipment comprising their control systems.


The range of application of field sensors in various industrial spheres has been expanding from the monitoring of production facilities such as flowmeters to management of the health status of personnel at work sites. Accordingly, demand has recently arisen for safety management operations through remote measurement of the biological information of individual workers.

Toshiba Infrastructure Systems & Solutions Corporation has developed the MULiSiTEN™ MS100 wristband type sensor capable of achieving quantification of a worker’s heat stress level using biological data including the amount of activity and pulse rate in addition to environmental data including the temperature and humidity. We have ensured that the MS100 provides the reliability required in industrial settings by applying our proprietary hardware and software technologies for continuity of operation and prevention of data loss in the event of a communication failure. Users are thereby equipped with the necessary resources to comprehensively manage workers’ conditions in high-temperature environments by means of Internet of Things (IoT) technologies.


X-ray thickness gauges play a key role in rolling lines at steel and nonferrous metal plants, measuring the thickness of metal sheets in order to control product dimensions and quality. The failure of an X-ray thickness gauge in these rolling lines generally leads to a suspension of operations. In particular, about one day is required to replace and adjust an X-ray tube in the event of failure of its X-ray generator, causing a reduction in productivity. Demand has therefore been increasing in recent years for functions to facilitate the timely exchange of X-ray tubes by means of abnormality prediction.

Toshiba Infrastructure Systems & Solutions Corporation has developed an abnormality prediction system for X-ray generators based on its long accumulation of experience in the development of X-ray thickness gauges for rolling lines. This system provides the user with information about abnormalities predicted by a statistical analysis method, utilizing our proprietary algorithm based on patterns of abnormal conditions from the X-ray tube voltage and tube current data collected during operation. This contributes to a reduction in operational risks caused by failures and supports stable plant operations.

OGASAWARA Tokinori / ANDO Kazuma

To ensure safe and stable operations of industrial plants, it is necessary to measure all conditions of the processes to be managed in real time. In reality, however, a number of processes cannot be continuously measured. Demand has therefore been increasing in recent years for soft sensor technologies that can predict the conditions of such processes using only the available measurable data.

Toshiba Mitsubishi-Electric Industrial Systems Corporation has developed a soft sensor technology capable of predicting the process conditions of monitoring and control systems whose data cannot be continuously measured, utilizing a model constructed based on the results of learning of past operation data in real time. We have incorporated this soft sensor technology into the PLANETMEISTER process information management database. Furthermore, the installation of this technology on controllers equipped with a central processing unit (CPU) module makes it possible to verify the reliability of process control.

OKAMOTO Masayoshi / BABA Yutaka / SAKAMOTO Tadashi

Steel and nonferrous metal plants have achieved stability of operations and product quality as a result of advancements in the performance and reliability of control systems. However, time-based maintenance is still performed as the mainstream of maintenance activities. In order to disseminate systematic predictive maintenance, control equipment and motor drive equipment are required that can process and analyze large volumes of high-speed sampling data in real time.

The Toshiba Group has responded to this situation by developing a next-generation control system capable of performing predictive maintenance for steel and nonferrous metal plants. This system incorporates the Unified Controller Vm series typeS (hereafter abbreviated as typeS) controller and the TMdrive-10e3 motor driver, thereby providing high-speed processing and analysis capabilities.

Feature Articles


Manufacturing industries are implementing various measures to increase production volume and improve product quality while suppressing equipment investments by making best use of their existing facilities. In the case of production lines for semiconductor and storage devices, in which many processes are executed using multiple pieces of equipment installed in parallel, differences in equipment performance tend to affect product quality. Therefore, equipment for which there are actual data showing smaller machining errors is being selectively used, taking into consideration the tradeoff between productivity and yield rate.

In order to rectify this situation, Toshiba Corporation has developed an equipment combination optimization method to improve the yield rate without reducing productivity in mass-production lines. This method makes it possible to rationalize the combination of equipment through the following processes: (1) determination of important processes that contribute to improvement of the yield rate based on actual data when specific pieces of equipment are combined, and (2) optimization of the combination of equipment in multiple processes executed by equipment with and without actual performance data. We have confirmed the effectiveness of this method through the results of numerical simulation tests showing that it achieves increases in the number of good-quality products by 5 to 20% compared with the conventional method.

UEDA Koji / SASAGAWA Kenji / FUJITA Takashi

In electricity infrastructures including power plants and substations, there is a growing need for autonomous systems to handle part of the patrol inspection work in order to address the labor shortage accompanying the declining birthrate and aging of society in recent years.

As a solution to this issue, Toshiba Energy Systems & Solutions Corporation has developed autonomous patrol robots that can automatically collect image data of target instruments while traveling along a predetermined patrol route at regular intervals. These robots incorporate the following functions: an autonomous movement function making it possible to travel through narrow passages, an imaging function to reliably capture images of target instruments, and an interruption function to stop the patrol work and return the robot to the base station in the event of an abnormality. Experiments using prototype robots for indoor and outdoor use equipped with the above-mentioned functions have confirmed the feasibility of realizing basic autonomous patrol tasks by means of these robots.

TSUZAKI Osamu / FUJIOKA Atsushi / TAUCHI Akihiko

Effective methods for inactivating bacteria and viruses by irradiation with ultraviolet (UV) light have recently become a focus of attention as a preventive measure against COVID-19.

Toshiba Lighting & Technology Corporation has developed UVish, a device that facilitates the suppression of viruses, eradication of bacteria, and deodorization in order to offer clean environments satisfying the various needs of society coexisting with COVID-19. Incorporating a UV light-emitting diode (UV-LED) with a peak wavelength of 280 nm and photocatalytic technologies using visible-light-responsive titanium oxide (TiO2), UVish provides high antiviral performance together with high bactericidal and deodorizing performance. This device also features a compact and lightweight housing allowing it to be easily installed anywhere and a photocatalytic filter unit that can be repeatedly used, in addition to quiet operation.

DONIWA Kenichi / TANAKA Takahiro / HARUKI Kosuke

Continually rising healthcare costs have become a social issue in Japan accompanying the increase in the number of patients suffering from lifestyle-related diseases due to the rapid growth of the elderly population since the 1970s. Various companies are consequently focusing on health promotion activities based on preventive medical care with the aim of securing the health of their employees, which affects economic and business activities.

In order to suppress the growth in healthcare costs and accelerate the dissemination of preventive medical care, Toshiba Corporation is promoting advances in the area of precision medicine aimed at supporting people’s healthy lives. As part of these efforts, we have developed the following two artificial intelligence (AI) solutions based on machine learning using information collected in annual health examinations: (1) a disease risk prediction AI solution that can predict the risk of developing lifestyle-related diseases with high accuracy, and (2) a lifestyle improvement AI solution that can facilitate behavioral changes by providing objective health guidance based on predicted risks.

OHASHI Teruyuki / KONO Hiroshi / IIJIMA Ryosuke

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) are attracting attention as a new generation of power devices due to their superior characteristics. Schottky barrier diode (SBD)-embedded MOSFETs have also been expanding into the mainstream due to their ability to reduce performance degradation caused by the operation of parasitic pn diodes in devices. However, the maximum current density at which the operation of parasitic pn diodes does not occur (hereafter abbreviated as Jumax) decreases with rising temperature, creating a problem for the high-temperature application of SBD-embedded SiC MOSFETs.

In order to prevent Jumax from decreasing at high temperatures, the Toshiba Group has developed a novel design method for SBD-embedded SiC MOSFETs that makes it possible to simulate Jumax using a simple structure model and design devices with an enhanced value of Jumax. Experiments on prototype 3.3 kV SBD-embedded SiC MOSFETs with structures designed by this method have confirmed that these structures achieve 4.7 times higher Jumax compared with the conventional structure.

Frontiers of Research & Development

Time-Series Waveform Anomaly Diagnostic Methods Utilizing Learning Shapelets for Infrastructure and Manufacturing Fields

*Company, product, and service names appearing in each paper include those that are trademarks or registered trademarks of their respective companies.