Czesław Cempel

Simple condition forecasting techniques in vibroacoustical diagnostics

Abstract Uses of vibroacoustical signals and related discriminants for machinery diagnostics are presented. The problem of forecasting machine failure is addressed and appropriate models and procedures are discussed. A high power fan bearing and a railroad diesel engine are used as illustrative examples.

Vibroacoustical diagnostics of machinery: An outline

Abstract The paper presents an outline of the methods and techniques created and applied in the vibroacoustical (VA) diagnostic area. After a brief introduction which shows the place of VA diagnostics in technical diagnostics and at different stages of the machinery lifetime, VA process generation and the link with machinery faults is shown. It enables the creation or choice of a proper VA symptom to a given fault and the establishment of the most effective type of “condition-symptom” relation for a given case. The next stage is diagnostic inference i.e. condition recognition and forecasting where the confidence level depends on the applied model and the inference method. Finally the author gives his views on future trends.

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.

Noise Reduction of Spiral Ducts

The paper presents noise reduction (NR) of spiral ducts as a result of computational modeling of acoustic wave propagation. Three-dimensional models were created with the finite element method in COMSOL Multiphysics version 3.3. Nine models of spiral ducts with 1–9 spiral leads were considered. Time-harmonic analysis was used to predict NR, which was shown in spectral and interval frequency bands. Spiral duct performance can be seen as a comparison of NR before and after a change from a circular to a spiral duct.

Extraction Methods of Multi - Fault Information in Machine Condition Monitoring

An outline of the multi-fault diagnostic observation concept is pres ent d in the paper. Starting from the commonly used Singular Value Decomposition (SVD) it is shown, that it is equivalent to the modern version of Principal Component Analysis (PCA) . During the development of the concept of multi-fault observation several indices and measures obtained from SVD were tried, and some seem to bring the success. This problem is also addre sse in the paper and illustrated by real examples taken from railroad Diesel engine condition m onitoring. Introduction Advances in the metrology of mechanical and dynamic processes, and o ur growing computational ability, allow us to consider the problem of simultaneous observation of multidimensional fault space evolving in the life of operating machines and equipment. What we measure in any multidimensional observation vector, are symptoms of the machine lif e and the advancement of cumulative multi fault damage. In very many cases of machine heal th monitoring, we know from the previous records, which part of the machine and what type of symptom sh uld be monitored and classified according to some standards or critical values, comi ng from long practice. Normally for critical machines we have many critical values for each critical part, and specific direction of vibration. So, we measure horizontal and vertical vibration of the bearing caps, machine and motor casing, and some other physical symptoms, like temperature, et c. Due to this situation of multi symptom observations for multi-fault monitoring of critical machi nes, the first author has proposed in DAMAS99 [1], the application of singular valued decomposition (SVD) f or the extraction of multi-fault information from the symptom observation matrix (SOM). This matrix consist of measured symptoms as columns, and each lifetime observation gives the successive row of the matrix. Usually the symptom observation matrix (SOM) has 10 or mor e c lumns, and 20 or more rows as lifetime observations. There were several problems to solve, to enable the optimal extr ction of multi fault information from the SOM, like centering of the symptom readings, and its normal ization at the beginning. The other problem was to find the best fault indices obtained from SVD, which describe in the best way the momentary fault evolution, as well as their life (damage) a dvancement. It was shown in a several research papers that the evolution of the first singular vectors and associated singular values gives good mapping of the fault evolution. The similar problem of a choice of the best symptom from many ot hers was considered by the first author in 1980, at that time by the use of principal component analysis (PCA). So the problem arise now, maybe the PCA is the better suited for the mult idi ensional fault information extraction, in comparison to SVD method ? It was shown in this paper t heo etically, and practically on some condition monitoring cases, that both approaches are equivalent. Thi s is because in a modern computational environment, like for example Matlab , the modern PCA algorithm is basing on SVD. But using PCA gives the same results in a machine fault space, and more quickly almost on line to system monitoring. What we need, in further development of this idea, is to learn fault interpretation of SVD and PCA results, and to give them some standar dization in terms of alarms and other critical values. There is also the need to include in the proc ssing of SOM some additional knowledge on machine operation, and the behavior of symptoms (observability ) in the process of condition monitoring. It seems to be possible in the future by using g e eralized SVD/PCA concepts Key Engineering Materials Online: 2003-07-15 ISSN: 1662-9795, Vols. 245-246, pp 215-222 doi:10.4028/www.scientific.net/KEM.245-246.215

Machine vibroisolation and dynamic loads of bearings

Abstract In this paper the effect of machine vibroisolation on the dynamic forces acting through bearings is investigated. An analytic solution is reached for the simple model of the rotating machine consisting of the rigid machine body and rigid rotor. These are connected by means of the bearins, and elastically jointly supported on a rigid base (foundation). It is further assumed that vibratory motion of the machine is caused by the static imbalance of its rotor. It is shown by analysis in this paper that the dynamic forces in the bearings of the vibroisolated machine are much higher, in a certain range of the input frequency rotation, than in the case of a machine with a rigid base. A design relation has been determined which satisfies the vibration isolation condition, and simultaneously decreases the dynamic load of the bearings. This can considerably increase the durability and safety of the machine.

Innovative multilevel energy processor in machine condition monitoring

Working machine can be considered as an equivalent energy processor (EP) with internally limited energy dissipation. This means, we have limited machine lifetime and breakdown time, and the observed vibration symptoms of machine condition can be incorporated into EP model. Up to now one level of EP model have been in use, but using Singular Value Decomposition (SVD) we need two levels EP, and due to this it is possible to trace several independent faults of machine during its life. This possibility allow us to formulate concept of machine as a multilevel energy processor, and using SVD we determine the second sublevel of this multilevel EP. The present paper illustrates this way of thinking, and allows us to deepen our understanding of machine life and the wear on two levels; overall machine damage and particular functional faults as well.

Optimization of symptom observation matrix in vibration condition monitoring

In diagnostics of complex machines, for their condition assessment we often use many ‘would be’ symptoms at the beginning, especially at the diagnostic startup of a new machine. The discrete observation of this ‘would be’ symptom vector creates so called symptom observation matrix (SOM). Using next the singular value decomposition (SVD) for the given SOM, one can extract the generalized fault symptoms, describing the fault evolution in a given case, and also diagnostic contribution of measured symptoms. Using the symptom reliability concepts further, and the grey system forecast methodology, it is possible to asses the generalized symptoms limit value. In this way one can establish the needed dimensionality of symptom observation matrix, and moreover assess the residual system life. However, doing this we have to establish new criteria for the dimensionality of SOM, based not on the number of symptoms in use, but the quality of diagnostic decision. This concept was verified in the paper using the data taken from real cases of vibration condition monitoring practice.

Decomposition of Symptom Observation Matrix and its Optimization in Vibration Condition Monitoring of Machines

With the modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of measuring quantities can be symptoms of machine condition. On this basis we can form the symptom observation matrix (SOM) intended for condition monitoring. On the other hand we know, that contemporary complex machines may have many modes of failure, so called faults. The paper presents a method for the extraction of fault information from the symptom observation matrix by means of singular value decomposition (SVD) in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM was applied with success. The attempt to assess the diagnostic contribution of primary symptom was undertaken, and also some approach to connect SVD methodology with neural nets is considered. These possibilities are illustrated in the paper by processing data taken directly from the vibration condition monitoring of the machine.

Evaluation of Diagnostic Methods

In this brief paper we present some comparisons of diagnostic methodologies. Our aims are to enhance our understanding of individual approaches, to indicate the possibilities for combining them and to anticipate possible extensions and new lines of investigation. Our choice of subjects unavoidably reflects our own interests and areas of expertise. We aim not to resolve the conflicts between competing philosophies but rather to collect them under a single roof, “since the human mind has already learned to deal in contradictions” [1, p489].