Kenneth A. Loparo

Stochastic stability properties of jump linear systems

Jump linear systems are defined as a family of linear systems with randomly jumping parameters (usually governed by a Markov jump process) and are used to model systems subject to failures or changes in structure. The authors study stochastic stability properties in jump linear systems and the relationship among various moment and sample path stability properties. It is shown that all second moment stability properties are equivalent and are sufficient for almost sure sample path stability, and a testable necessary and sufficient condition for second moment stability is derived. The Lyapunov exponent method for the study of almost sure sample stability is discussed, and a theorem which characterizes the Lyapunov exponents of jump linear systems is presented. Finally, for one-dimensional jump linear system, it is proved that the region for delta -moment stability is monotonically converging to the region for almost sure stability at delta down arrow 0/sup +/. >

Bearing fault diagnosis based on wavelet transform and fuzzy inference

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations.

Stabilization of continuous-time jump linear systems

We investigate almost-sure and moment stabilization of continuous time jump linear systems with a finite-state Markov jump form process. We first clarify the concepts of /spl delta/-moment stabilizability, exponential /spl delta/-moment stabilizability, and stochastic /spl delta/-moment stabilizability. We then present results on the relationships among these concepts. Coupled Riccati equations that provide necessary and sufficient conditions for mean-square stabilization are given in detail, and an algorithm for solving the coupled Riccati equations is proposed. Moreover, we show that individual mode controllability implies almost-sure stabilizability, which is not true for other types of stabilizability. Finally, we present some testable sufficient conditions for /spl delta/-moment stabilizability and almost-sure stabilizability.

Stochastic stability of jump linear systems

In this note, some testable conditions for mean square (i.e., second moment) stability for discrete-time jump linear systems with time-homogenous and time-inhomogenous finite state Markov chain form processes are presented.

Machine diagnostic system and method for vibration analysis

A diagnostic system includes a vibration sensor mounted on a machine to measure vibrations. Vibration signals from the sensor are processed and analyzed by the system. From a known critical frequency of a vibration-generating component, the system measures the amplitude of the vibration signal at more than one harmonic frequency of the known critical frequency and compares the amplitudes to amplitudes at adjacent harmonic frequencies. When a relatively large amplitude is found at a harmonic frequency, that is a harmonic frequency near a resonant frequency of the physical path between the vibration sensor and the vibration-generating component, the system analyzes the shape and magnitude of the vibration signal around that harmonic frequency to evaluate the condition of the machine.

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models (HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.

Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data

Abstract This paper presents two separate algorithms for estimating the running speed and the bearing key frequencies of an induction motor using vibration data. Bearing key frequencies are frequencies at which roller elements pass over a defect point. Most frequency domain-based bearing fault detection and diagnosis techniques (e.g. envelope analysis) rely on vibration measurements and the bearing key frequencies. Thus, estimation of the running speed and the bearing key frequencies are required for failure detection and diagnosis. The paper also incorporates the estimation algorithms with the most commonly used bearing fault detection technique, high-frequency demodulation, to detect bearing faults. Experimental data were used to verify the validity of the algorithms. Data were collected through an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2HP IQPreAlert)-driven mechanical system. Both inner and outer race defects were artificially introduced to the bearing using electrical discharge machining. A linear vibration model was also developed for generating simulated vibration data. The simulated data were also used to validate the performance of the algorithms. The test results proved the algorithms to be very reliable.

Analysis of the value for unit commitment of improved load forecasts

Load forecast errors can yield suboptimal unit commitment decisions. The economic cost of inaccurate forecasts is assessed by a combination of forecast simulation, unit commitment optimization, and economic dispatch modeling for several different generation/load systems. The forecast simulation preserves the error distributions and correlations actually experienced by users of a neural net-based forecasting system. Underforecasts result in purchases of expensive peaking or spot market power; overforecasts inflate start-up and fixed costs because too much capacity is committed. The value of improved accuracy is found to depend on load and generator characteristics; for the systems considered here, a reduction of 1% in mean absolute percentage error (MAPE) decreases variable generation costs by approximately 0.1%-0.3% when MAPE is in the range of 3%-5%. These values are broadly consistent with the results of a survey of 19 utilities, using estimates obtained by simpler methods. A conservative estimate is that a 1% reduction in forecasting error for a 10,000 MW utility can save up to

Neurophysiologic assessment of brain maturation after an 8-week trial of skin-to-skin contact on preterm infants

1.6 million annually.

A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals

OBJECTIVE Skin-to-skin contact (SSC) promotes physiological stability and interaction between parents and infants. Analyses of EEG-sleep studies can compare functional brain maturation between SSC and non-SSC cohorts. METHODS Sixteen EEG-sleep studies were performed on eight preterm infants who received 8 weeks of SSC, and compared with two non-SSC cohorts at term (N=126), a preterm group corrected to term age and a full-term group. Seven linear and two complexity measures were compared (Mann-Whitney U test comparisons p<.05). RESULTS Fewer REMs, more quiet sleep, increased respiratory regularity, longer cycles, and less spectral beta were noted for SSC preterm infants compared with both control cohorts. Fewer REMs, greater arousals and more quiet sleep were noted for SSC infants compared with the non-SSC preterms at term. Three right hemispheric regions had greater complexity in the SSC group. Discriminant analysis showed that the SSC cohort was closer to the non-SSC full-term cohort. CONCLUSIONS Skin-to-skin contact accelerates brain maturation in healthy preterm infants compared with two groups without SSC. SIGNIFICANCE Combined use of linear and complexity analysis strategies offer complementary information regarding altered neuronal functions after developmental care interventions. Such analyses may be helpful to assess other neuroprotection strategies.