Predictive maintenance, in which equipment deterioration and defects are detected in advance for management in an optimal state, is an increasingly important factor in the continued safe and secure use of products and systems. The market for predictive maintenance solutions is in the growth stage, reaching about $6.9 billion in 2021, with projections for rapid growth at a compound annual growth rate of 31% to about $28.2 billion worldwide in 2026(*2).
As an infrastructure services company, Toshiba is working to reduce interruptions and downtime due to failures in equipment that support social infrastructure, and to establish highly accurate predictive maintenance technologies that minimize maintenance costs. Anomaly detection and suggestions for solutions are indispensable aspects of predictive maintenance. However, many infrastructure devices generate abnormalities via complicated mechanisms, and an outstanding problem is the difficulty of formulating improvement measures by using conventional anomaly detection technologies that only show differences from the normal state. Taking appropriate measures and reducing maintenance costs will require technologies that can explain not only what is different from normal, but the causes of such differences.