Condition monitoring and fault detection of inverter-fed rotating machinery

Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery

Conserving Energy with No Watt Left Behind

Facilities managers for industrial and commercial sites want to develop detailed electrical consumption profiles of their electrical and electromechanical loads, including expensive physical plant for heating, ventilation, and air conditioning (HVAC) and equipment for manufacturing and production. This information is essential in order to understand and optimize energy consumption, to detect and solve equipment failures and problems, and to facilitate predictive maintenance of electromechanical loads. As energy costs rise, residential customers are also developing a growing interest in understanding the magnitude and impact of their electrical consumption quickly, easily, and informatively

Modeling and condition monitoring of fully floating reciprocating compressor main bearings using data driven classification

Condition monitoring reduces maintenance costs on industrial machinery by reducing downtime and allowing for need-based maintenance instead of schedule-based maintenance. Currently, condition monitoring is not as widely applied on reciprocating compressors as compared to rotating compressors. However, research for monitoring various components of reciprocating compressors such as inlet and outlet valves and piston rings is conducted. There is industry interest into expanding this research to the main bearings of the compressor. Previous research on bearings focuses on either rolling element bearings or traditional journal bearings with not much information available on low speed applications of fully floating ring journal bearings as are studied in this work. The following work shows a detailed derivation of the forces acting on the main bearings during normal compressor operation based on kinematic relations and dynamic equivalence. The bearing is simulated using an adaptation of the mobility method for fully floating ring bearings found in previous research. It involves solving two simultaneous mobility calculations along with the ring speed to link the inner to the outer bearing. Experimental data of the crankshaft orbit is collected for comparison to the simulation. Condition monitoring for three different fault types is investigated through seeded fault testing: Varying lubricant viscosity, oil feed hole obstruction, and grooves in the bearing land. Principle component analysis has been shown previously to be a successful method of feature selection for classification. This is applied to several sensors and the classification results are compared. A single axis position measurement of the crankshaft shows the most promising results compared to a traditional accelerometer on the bearing housing and a novel accelerometer on the crankshaft. The single axis measurement provides a cost efficient alternative method to the two axis orbit measurement typically used for traditional journal bearings

Non-intrusive fault detection in reciprocating compressors

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 128-130).This thesis presents a set of techniques for non-intrusive sensing and fault detection in reciprocating compressors driven by induction motors. The procedures developed here are "non-intrusive" because they rely only on electrical measurements to reconstruct the mechanical signals internal to the compressor. This allows for easy and non-intrusive determination of many fault sensitive signals that usually require complicated, expensive, and time consuming operations to measure. A sample of the signals produced by the procedures of this thesis are estimates of the cylinder suction and discharge pressures and a composite torque signal containing the effects of the mechanical loads within the compressor. This load torque signal is especially sensitive to faults, and a demonstration of the effect on and detection of compressor valve faults from the load torque signal is given. One of the key steps in the algorithm presented here is a procedure to "invert" the induction motor dynamic model equations to allow direct calculation of motor shaft speed and torque from stator current and voltage measurements. For this procedure a non-intrusive method to estimate motor model parameters from an in-situ induction motor driving a periodic load was developed.by Christopher James Schantz.S.M

Condition monitoring of reciprocating compressor valves using analytical and data-driven methodologies

Condition-based health monitoring systems are a very important addition to machinery to monitor the system and assure it is running at the peak efficiency, to schedule maintenance, and prevent catastrophic failure. Many times these systems are combined with different sensors to predict when service is required for different wear parts and this keeps the machine running optimally. An accurate prediction of health is accomplished by measuring and analyzing different critical parameters and detecting when these parameters deviate from the nominal values. Recently, these systems have started to become more common on industrial compression technology. Typically, reciprocating compressor health monitoring systems only use indirect measurements, P-V diagrams, to monitor the health of the system. This research focuses on improving these monitoring systems. Specifically this research will focus on three different valve failure modes that are common in reciprocating compressors. They are liquid slugging, valve spring fatigue, and valve seat wear. These faults are investigated first through a system level model to better understand how different subsystem dynamics are related through the compressor. Also an instrument investigation is conducted to determine what types of sensors are the most effective at detecting these faults. The Bayesian classification method is used in conjunction with seeded fault training data to create a classifier that can determine the state of health of the machine. The classification approach can be integrated into health monitoring software to be used in different reciprocating compressors

Refrigeration System: Capacity Modulation Methods

Energy conservation and reduction of the global warming effect become one of the most important subjects in the world. Since refrigeration system energy consumption is steadily increasing in overall energy consumption, this system is under research. Refrigeration systems are full of energy conservation that is having minimum energy consumption while satisfying the user’s needs. Refrigeration system applications where the load may vary over a wide range, due to lighting, product loading, ambient weather variations, or other factors during operation, can be optimized by capacity modulation. There are many ways to achieve capacity modulation. This paper presents literature review of various capacity modulation methods which reduce the energy consumption of the refrigeration system and decrease CO2 emission indirectly. In this paper, on/off control, digital scroll compressor, cylinder unloading, hot gas bypass, slide valve, multiple compressor, and variable speed capacity control methods are presented. In addition, electrical control techniques for the refrigeration capacity modulation applications are summarized