Intelligent Techniques for Condition Monitoring of Rolling Mill Jyrki Tervo, Mikko Mustonen, Risto Korhonen* VTT Manufacturing Technology P.O.Box 1702, FIN-02044 VTT, Finland Phone: +358-9-4561, Fax: +358-9-460 627 email: {jyrki.tervo, mikko.mustonen}@vtt.fi * Rautaruukki Steel P.O.Box 93, FIN-92101 Raahe, Finland Phone: +358-8-849 2541, Fax: +358-8-849 3182 email:
[email protected]
ABSTRACT: The condition monitoring and diagnosis of a steel rolling mill requires a variety of measurement techniques. Methods such as vibration measurement, acoustic emission, hydraulic oil analysis, pressure and temperature measurements are frequently applied. Since any single intelligent technique may fail in analysing the required data, an approach to the development of a hybrid expert system has been made. Hybrid expert systems are reported to be the most robust and powerful diagnostic tools for condition monitoring, because the hybrid systems use multiple intelligent techniques appropriate to the problem. The development procedure includes collection and analysis of fault data. This can be done by fault simulations with a computer, in a test rig or by collecting the true fault data from the plant. Other requirements evolve from the environment and the history of the rolling mill. An expert system needs to be modular, cheap and easy to maintain, as well as easily adaptable to the existing measurement and data acquisition system. In the present case the measurements are being saved to files and transferred to a file server by file transfer protocol (FTP)client application. The measurements are then analysed and the resulting parameters of symptoms, features and characteristics are saved into a database. The expert system makes conclusions and sends reports to privileged users. KEYWORDS: Hybrid expert system, condition monitoring, steel rolling mill, vibration, acoustic emission, hydraulic oil, temperature
INTRODUCTION Steel rolling is an operation whereby slabs of variable size are rolled into steel plates of certain thickness, width, length, flatness, shape, surface quality and temperature. Rolling is the key operation affecting plate quality. Roll gap adjustment is a two-stage process including rough adjustment and fine-tuning. The rough adjustment is performed with a screwdriven mechanism. The fine-tuning of the roll gap is made with a hydraulic system. Hydraulics is also used to perform rolling in different modes. An important stage in the rolling is the automatic adjustment of plate shape, which means a variation of the slab thickness profile to minimise the variation in the plate symmetry. The hydraulic system of the rolling mill is presented in Figure 1. Condition monitoring and diagnosis of a steel rolling mill requires a variety of measurement techniques. The hydraulic system is included in the category of process automation. The automation data can also be used for the system diagnosis. The different signals in the roll hydraulic system control include cylinder position, rolling force, rolling pressure, position error and servo valve stem position, servo command signals, screw position, roll velocity and motor current. The process automation data is not enough for diagnosing such complex machinery as a plate rolling mill. Somewhat more specific measurements are also required. Therefore methods such as vibration measurement, acoustic emission and hydraulic oil analysis need to be applied. Some extra pressure and temperature sensors are attached to the "hot spots" of the hydraulic system.
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Figure 1. Principles of the hydraulic control system of the Rautaruukki hot rolling mill.
COMMON FAULTS AND PROBLEMS IN HYDRAULICS The steel plate hot rolling process is affected by the functioning of the hydraulic system of the mill. The main elements of the hydraulic system are hydraulic fluid, filters, pumps, motors, control valves, cylinders, accumulators, reservoirs, seals, and the electronic control devices. Most of the faults in hydraulic systems are due to fluid impurities. Especially servo valves are delicate devices and require utmost cleanliness. Some common faults of hydraulic systems are listed in Table I. Symptoms /Causes Pump noisy Fluid heated Erratic flow Low pressure Erratic pressure Erratic movement Erratic speed
Cavitation
Air in fluid
System pressure Flow setting
Relief valve setting Relief valve setting Pressure limit setting Damaged relief valve Poor lubrication Feedback transducer
Leak Air in fluid Erratic pressure Erratic flow
Coupling misaligned Dirty fluid
Pump damaged Low supply
Partially open valve Pressure limit damaged Dirty fluid
External leak
Erratic signal Servo amplifier
Damaged pump Defective accumulator Servo amplifier Overriding workload
Incorrect viscosity Damaged component Damaged motor Damaged pump Feedback transducer
Faulty cooling Pump RPM Damaged cylinder Damaged motor Sticking servo
Damaged component
Damaged cylinder
Table I. Some common symptoms and causes of faults in hydraulic systems (Hehn, 1995).
DATA ACQUISITION AND SIGNALS The data acquisition system is summarised in Figure 2. Sensors, instruments and accessories are selected depending on the measured variables. Necessary signal processing methods (such as amplification, isolation, potential transformation,
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modulation and filtering) have been specified and attached. Measurement software takes care of the data acquisition and required mathematical pre-processing. Data type dependent analysis software performs signal analysis procedures, such as FFT and wavelet transformation and calculates key figures. Finally the expert system analyses the pre-processed data from databases and draws conclusions on the system status. In this case the expert system is a hybrid because rule-based reasoning methods and neural networks are being applied.
Graphic userinterface
Processautomation
Measurement browsers in local network
Database Steel Works Network (TCP-IP)
File server
Measurement substation Measurement substation
Analysis substation
Expert system
Figure 2. Principle of data acquisition system for the Rautaruukki Steel Works. The measurements to be used for fault diagnostic purposes are listed in Table II. The quality and quantity of the diagnostic measurements must be specified by a system fault analysis, also considering that some of the faults can only be found by combining the data from multiple measurements. The aim is to keep the number of sensors as low as possible to avoid extra work and cost. Measurement Main pressure line Servo valve stem Filter Oil temperature Component temperature Vibration Acoustic emission
Sensor / System Pressure sensor LVDT Pressure sensor Temperature sensor Temperature sensor Piezoelectric accelerometer Piezoelectric sensor Dielectric sensor
Signal type Analog voltage Analog voltage Analog voltage Analog voltage Analog voltage Analog voltage Analog voltage and digital threshold switch Analog voltage
Range 0 - 10 V 0 - 10 V 0 - 10 V 0 - 10 V 0 - 10 V 0 - 10 V 0-5V 0 or 5 V 0-5V
Oil water content, TAN, solid particles, viscosity, refractive index Oil solid contamination
Online particle counter
RS-232
ASCII
Table II. Different signal types. (LVDT = Position sensor, linear variable differential transformer type; TAN = Total acid number)
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DIAGNOSTICS An approach to the development of a hybrid expert system has been made. Hybrid expert systems are reported to be the most robust and powerful diagnostic tools for condition monitoring, since hybrid systems mix intelligent techniques appropriate to the problem. The development procedure includes the collection and analysis of fault data. This can be done by fault simulations with a computer, in a test rig or by collecting the true fault data from the plant. Other requirements evolve from the environment and the history of the rolling mill. An expert system needs to be modular, cheap and easy to maintain, as well as easily adaptable to the existing measurement and data acquisition system. In the present case the measurements are being saved to files and transferred to a server by an FTP client application. Measurements are then being analysed and the resulting parameters of the symptoms, features and characteristics are saved into a database. The expert system draws conclusions and sends reports to privileged users. A hybrid system can be built up by combining expert systems and neural networks (Lemmen, Svaricek, 1994). The expert system can be used as a means to select relevant inputs for the neural network, as well as to check the validity of the neural network output. The expert system can also be used as an off-line tool in a qualitative way. The user selects the observed symptoms and the expert system offers the logic and the mechanics for the problem solving. Neural networks are numeric, associative and self-organising by nature. Measured information directs the calculation (Kohonen, 1998). Correlation between the parameters can be taught to the network directly from the signals, observations or statistics. A neural network consists of neurones, each having several inputs and one output. Specific inputs can be weakened or strengthened by weight coefficients. By applying Kohonen networks (self-organising map, SOM), other than specified changes in machinery operation may also be detected. It is also possible to get a prognosis on component fault development in the machinery, which gives crucial time for maintenance planning. Tuning of neural networks requires lots of high quality failure data (Jantunen et al., 1997). Expert systems can be used to interpret symptoms in situations where learning requires numerous measurements from faulty machinery. An existing problem with expert systems is that they need to be application specific. Each machine is an individual. Therefore generating and maintaining a list of symptoms and their causes requires a lot of work and machinery specific expertise. Even if the list of symptoms and causes can be generated from common sources, there is still a problem with final tuning. Actual fault revealing messages need to be extracted from erroneous messages, and robust alarming signal levels need to be set. Computer simulations may be used for reproduction of the erratic function of machinery (Iserman, 1997). This requires modelling of machinery behaviour, which also requires lots of work. Models should also be linear, rather than nonlinear, in order to avoid mathematical difficulties. Hydraulic systems are not linear, which causes difficulties and restricts the use of model-based diagnostics. However, existing simulation models can also be used for the machine diagnostics, applying them for a continuous on-line comparison between real and modelled behaviour. Diagnostic systems should be highly visual and illustrative to the user, and should have explicitly defined controls in order to be user friendly and safe to operate. The average user should not have to be an expert. However, experts are needed for rule development and upkeep as well as upgrading of the system. Therefore two kinds of access categories are needed – for users and experts. System upgrading and so forth can be done offline, while machine diagnosis should be performed continuously online. Finally, the system should have the ability to forecast the final rupture of the component or the machinery, so that corrective measures can be taken accordingly.
SUMMARY A single intelligent technique may fail in the analysis of the required data, thus an approach to the development of a hybrid expert system has been made. Hybrid expert systems are powerful tools in condition monitoring and diagnosis of machinery since they provide a mixture of intelligent techniques, thereby complementing each other. The development procedure includes collection and analysis of fault data. This can be done by fault simulations with a computer, in a test rig or by collecting the true fault data from the plant. Other requirements evolve from the environment and the history of the rolling mill. An expert system needs to be modular, cheap and easy to maintain, as well as easily adaptable to the existing measurement and data acquisition system.
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REFERENCES Harris, T., Artificial Neural Networks in Condition Monitoring. Handbook of condition Monitoring (ed. Rao, B.), 1996, Elsevier Science Ltd, pp. 341 - 348. Hehn, A., Fluid Power Troubleshooting. Marcel Dekker Inc, second edition, 1995, 647 p. Iserman, R., Fault-detection and Fault-diagnosis Methods. Control Engineering Practice, Vol. 5 (1997), pp. 639 - 652. Jantunen, E., Vähä-Pietilä, K., Halme, J., Katajamäki, K., Virtanen, J., Volkov, T., Simulation of Faults in Rotating Machines. Proceedings of the 10th International Conference on Condition Monitoring and Diagnostic Engineering Management, COMADEM '97, VTT Symposium 172, Espoo, Finland, 1997, pp. 283 - 292. Kohonen, T., The Self-Organising Map. Neurocomputing, Vol. 21 (1998), pp. 1 - 6. Lemmen, R., Svaricek, F., Multistage Diagnosis of Hydraulic System. Proceedings of SICE '94, Tokyo, July 26 - 28, 1994, pp. 1009 - 1012.
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