System integration in condition based maintenance (CBM) is one of the biggest challenges that need to be overcome for widespread deployment of the CBM methodology. CBM system architectures investigated in this work include an independent monitoring and control unit with no communication with machine control (Architecture 1) and a data acquisition and control unit integrated with the machine control (Architecture 2). Based on these architectures, three different CBM system applications are discussed and deployed. A verification of the third system was done by performing a destructive bearing test, causing a spindle to seize due to lubrication starvation. This test validated the CBM system developed, as well as provided insights into using sensor fusion for a better detection of bearing failure. The second part of the work discusses intelligence in a CBM system using a Bayesian probabilistic decision framework and data generated while running validation tests, it is demonstrated how the Naïve Bayes classifier can aid in the decision making of stopping the machine before catastrophic failure occurs. Discussing value in combining information supplied by more than one sensor (sensor fusion), it is demonstrated how a catastrophic failure can be prevented. The work is concluded with open issues on the topic with ongoing work and future opportunities.

This paper describes the dynamic characteristics of a newly-designed force sensor comprised of carbon nanoparticles embedded in a polyphenylene sulfide matrix and operating on the principle of contact resistance change with pressure. Sensor performance was investigated for frequencies ranging from 1 to 1,000 Hz using two testing setups: a load frame for low frequency characterization and a piezo-electric stack for describing higher-frequency behavior. Bode magnitude and phase response plots were developed and it was determined that the sensor under study can be modeled as a first order system up to 600 Hz. The −3 dB bandwidth was found to be 90 Hz and the sensor’s time constant was determined to be 0.0018 seconds. A dynamic model of the sensor is constructed and compared against performance data. The sensor was found to have non-linear spring properties, allowing for two damping coefficients, one for each spring constant range, to be calculated. The damping coefficient was calculated to be 619 lb-s/in for loadings under 600 lbs and 1928 lb-s/in for loadings greater than 600 lbs. The sensor’s time response was also found to be more similar in shape to the input loading waveform when it was compared to piezoelectric load transducers.

Condition based maintenance (CBM) of machine tools is an important maintenance strategy to invoke for a manufacturing company to run as lean as possible. CBM does this by indicating, in advance, the failure of the machine tool components or system, thus reducing the machine downtime. In this paper, the development of such a system is sought. A background review of the need and structure of such a system has been provided as well as the design considerations for the system are discussed. Having those considerations as the target requirements for a CBM system, discussion of a demonstrative system is presented, being implemented on an OKUMA LB 3000EX CNC lathe. Leveraging the Open Architecture Control (OAC) technology built into OKUMA CNC systems, the proposed system shall enhance machine monitoring by integrating the internal and external sensors aboard the machine tool. This work lays the foundation for the framework of a proposed CBM system. Coolant temperatures and spindle vibration signals are acquired and processed using a high speed data acquisition system. Towards the end of the paper, descriptions of how to best use this data and integrate it with the machine tool CNC system have been provided.