As a simple observation of the world, it is composed of human beings, artifacts, and natural environment. As all of their
healths are issues like expansion of healthy aging, low maintenance cost, and low energy consumption the notion of
health management can be extended to be applicable to all the entities. In this article, health management technology is
proposed as a general solution framework. Its important aspect is cyclic evolution based on causality which illustrates
conditions of target systems. The causality can be used as problem-solving knowledge, which is composed of feature
attributes extracted from sensory data and intermediate characteristics. The causality should evolve to be updated
according to sophistication of sensing and control mechanisms. It also provides the important nature of transparency to
humans and machines bidirectionally, which enhances human-machine collaboration. Besides the idea of health
management technology, the applications of human health, manufacturing, and energy consumption are also introduced
and discussed. All applications were realized by multiple sensory networking to require multivariate time series analysis.
Some experiments were conducted to investigate the performance of the proposed method.