for more than a century, the induction motor (im) has been the powerhouse industrial applications such as machine tools, manufacturing facilities, pumping stations, and more recently, in electric vehicles. in addition, ims account for approximately 40%- 45% of the annual global electricity consumption. therefore it is a critical issue to improve im operation efficiency and reliability. in applications, unexpected failures of ims can result in extensive production loss and increased costs. the classical preventive maintenance procedures involve periodic stoppages of ims for inspection. if such procedures result in no faults found in the machine, as is common in practice, the unnecessary downtimes will increase operational costs significantly. this inefficiency can be addressed by condition monitoring, whereby sensors relay information about the im in real-time, allowing for incipient im fault diagnosis. such a process involves three general stages: • data acquisition: a process to collect data using appropriate sensors. • fault detection: a means to process collected data, extract representative fault features, and determine the condition of the motor components. • fault classification: a means to automatically classify fault data to allow decision-making on whether or not the motor is healthy or damaged. however, there are challenges with the above stages that are at present, barriers to the industrial adoption of condition monitoring, such as: • implementation limitations of traditional wired sensors in industrial plants. • the restrictive memory and range capabilities of existing commercial wireless sensors. • challenges related to misleading representative fault signals and means to quantify the fault features. • a means to adaptively classify the data without prior knowledge given to a fault classification system. to address these challenges, the objective of this work is to develop a smart sensor-based im fault diagnostic system targeted for real industrial applications. specific projects pertaining to this objective include the following: smart sensor-based wireless data acquisition systems: a smart sensor network including current and vibration sensors, which are compact, inexpensive, lowpower, and longer-range wireless transmission. • fault detection: a new method to more reliably extract the representative fault features, applicable under all im loading conditions. • fault quantification: a new means to transform fault features into a monitoring fault index. • fault classification: an evolving classification system developed to track and identify groups of fault index information for automatic im health condition monitoring. results show that: (1) the wireless smart sensors are able to effectively collect data from the induction motor, (2) the fault detection and quantification techniques are able to efficiently extract representative fault features, and (3) the online diagnostic classifier diagnoses the induction motor condition with an average accuracy of 99.41%.