Modeling and State Estimator Design Issues for Model-Based Monitoring Systems

Abstract

The JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL has a rich history in the areas of system modeling, control theory, and system design. In this paper, I demonstrate the primary importance of modeling to the design and performance of model-based (dynamic, observer-based) monitoring systems. The focus is on monitoring rather than diagnostics, but the results show, nevertheless, that good monitoring systems impact the design and performance of diagnostic systems. Modeling procedures, some based on results from the automated modeling literature, are developed to produce not only simple, accurate models to improve estimation accuracy, but also to utilize existing sensors to measure accessible signals, generate models for which estimation techniques exist, and finally to produce models that reduce the complexity of the diagnostic task, thus reducing the heavy reliance on empirical rule-based decision systems. In addition, for model-based monitors based on deterministic state estimators, a design procedure is described to control the shape of the transient error and the steady-state bias error of the estimates. This procedure utilizes a performance index, which accounts for many of the realistic problems encountered in implementing these systems, such as model uncertainty, round-off errors, and unknown initial conditions. Machine tool drive systems are used to motivate and illustrate the monitoring ideas and techniques. Model-based monitoring appears, despite its current limited use in machine monitoring, to hold great potential as the basis for high-performance, low-cost machine monitoring systems.

Spindle Housing Design Parameter Optimization Considering Thermo-Elastic Behaviour

“…10 µm ≤ x 3 ≤ 30 µm ( 1 5 ) 200 mm ≤ x 4 ≤ 350 mm (16) 100 mm ≤ x 5 ≤ 350 mm (17) 50 mm ≤ x 6 ≤ 100 mm (18) Final optimized values for spindle housing parameters are calculated numerically from the optimization problem defined in Eq. 10 to Eq.…”

“…Jay F. Tu and J.L. Stein [15][16][17][18][19] also suggested an on-line thermally induced preload monitoring using a dynamic state-space observer model. B. Bossmanns et al [20,21] presented a finite difference thermal model to characterize the power distribution of a high-speed motorized spindle.…”

Fault Detection for Gas Turbine Sensors Using I/O Dynamic Linear Models: Methodology of Fault Code Generation

“…The sensor fault detection techniques are those of Analytical Redundancy and Hardware Redundancy, in which the reliability of measurements is obtained by duplicating the hardware elements. In comparison to Hardware Redundancy, Analytical Redundancy requires the use of a smaller number of sensors, allowing reductions of the space required and costs, thereby making it of greater interest in the field of small and medium power size industrial gas turbines, for which cost reduction is of primary importance (Isermann, 1993;Stein, 1993).…”

Sensor-based Condition Monitoring and Predictive Maintenance-An Integrated Intelligent Management Support System

“…The control inputs could be modified to maintain the machine's performance up to specification, and the need for maintenance could be logged and even scheduled (Stein, 1993). These systems use a wide array of sensor technology, signal processing techniques to assess the current condition (or state) of the machine.…”

“…For example, a command may be issued to stop or perform certain maintenance actions on a machine. The control inputs could be modified to maintain the machine's performance up to specification, and the need for maintenance could be logged and even scheduled (Stein, 1993). Sensor data utilization and interpretation are important issues in the decision-making process for maintenance management.…”