4. Cost Effective Maintenance for Competitive Advantages
4.4 Effective condition based maintenance decision making
In the following we present the results obtained by solving the fourth research question, which is connected to Paper IV and Paper V in Appendix C. The fourth research question was stated as:
How to improve the effectiveness of condition based maintenance (CBM) decision-making?
Complex manufacturing systems are becoming more sensitive to disturbances, and cannot tolerate expensive and unpredictable behaviour. Therefore the availability and reliability of manufacturing systems are vital, and the importance of using effective decision-making systems is increasing. In order to ensure the optimum performance of automated manufacturing systems, various condition- monitoring techniques are used.
When dealing with vibration-based maintenance (VBM), the condition of significant parts (e.g. rolling element bearings) cannot be assessed effectively, i.e. with high certainty, without considering both probabilistic and deterministic aspects of the deterioration process. Modelling the time for maintenance action and predicting the value of the vibration level when damage of a significant component is detected are examples of the probabilistic part, which is discussed in Paper IV. However, issues related to machine function, failure analysis and diagnostics are examples of the deterministic part that is discussed in Paper V.
4.4.1 Mechanistic model for predicting the vibration level When a potential failure (damage under development) of a significant compo- nent is detected, predicting the value of the CM parameter, e.g. vibration level, during the interval until the next measurement or planned stoppage, accurately, would enhance the effectiveness of decision-making process. A model for pre- dicting the CM parameter, e.g. vibration level, during the next period and until the next measuring moment was developed by Al-Najjar (2001). In Paper IV we reformulated the model as illustrated by equation 4.1. Let Y be the dependent variable representing the predicted value of the vibration level. It is the function of three independent variables (X, Z & T) and three parameters (a, b and c). For i=1, 2…n, and i the number of measuring opportunities after damage initiation.
i c i i i i i
X
a
bT
Z
E
Y
i
1
exp(
1)
(4.1) Where 1 iY
:
The predicted value of the vibration level at the next planned measur- ing time.
1
i
T
:
The elapsed time since the damage is initiated and its development is detected.i
X :
The current vibration level value.i
Z :
The deterioration factor, i.e. the function of the current and anticipated future load and previous deterioration rate.a :
The gradient (slope) by which the value of the vibration level varied since it started to deviate from its normalstate (x ) due to initia-
otion of damage until detecting it atx .
pi
i
c
b &
:
Non-linear model’s constants.E
i:
The model error, which is assumed to be identical, independent and normally distributed with zero mean and constant variance, N (0,V). To test and verify the model two tests were performed. The first test was based on conducting an experiment at the department’s CM laboratory and the second test was based on real data collected from a Swedish paper mill. In both cases the model was able to predict the value of the vibration level accurately.Figure 4.11 shows the results achieved when testing the model using laboratorial experiment data. As we can see, the model can predict the value of the vibration level with an absolute error that ranges from –1.14 to 0.77 mm/sec with an error mean value of 0.15 and a standard deviation of 0.51. The model is capable of adapting to the changes that are taking place within the machine’s operating condition. It can be seen that the model learns from the experience encountered in the recent performance, i.e. the predicted value follows the trend and latest development in the recent vibration level actual value. For example, in measuring opportunity number eight the actual value was smaller than the
0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 - 1 0 1 2 3 4 5 6 7 8 9 1 0 1 1 XP= 4 . 0 m m / s e c Xo= 3 . 1 4 m m / s e c Y a c t u a l Y p r e d ic t e d E r r o r CM Para m e ter V a lue 1X RPM m m /sec M e a s u r in g o p p o r t u n it y in H o u r s , D a y s , W e e k s o r M o n t h s
predicted value (which could happen in real life applications), and therefore, for the next point (measuring opportunity number nine) the model was adapted and predicted a value that goes with the actual changes experienced recently, i.e. with a decreased rate. Then, again since the actual reading at measuring opportunity number nine was higher than the predicted value, the model adapted again and predicted a value with an increasing rate at point 10, and so forth. Furthermore, by eyeballing the trend for the actual and predicted points we can see that both sets of points can be fitted by more or less the same curve. We can see that the error, i.e. the dispersion between the predicted value of the vibration level and its actual value, is decreasing with time.
Fig. 4.11. The actual CM value, the predicted CM value and the error obtained when predicting the CM parameter value
To verify the model in a real life application, real vibration data collected about a spherical roller bearing installed at the lead roller of a paper mill machine were used. It was found that the model could predict the value of the CM parameter with an absolute error that ranges from –0.47 to 0.04 mm/sec with an error mean value of –0.18 and a standard deviation of 0.26. For more details see Paper IV
4.4.2 Improving the effectiveness of decision-making sys- tems
To improve the accuracy of decision making, more attention, in the literature, is paid to integrating and interpreting richer information from the fusion of multiple sensor data, reducing noise and obtaining the most reliable extracted features. But less attention was devoted to the integrated CM system, which enables the user to evaluate a multi-variant system based on the data collected from different
sources such as CM transducers, maintenance, quality, production, accountancy, machine tools and process monitoring. On the other hand, although expert sys- tems help analysts dealing with information and decision-making by accepting data from multiple sources, and then using a model and rule-based programming to diagnose a problem, we believe that there exist additional elements, which should be considered to increase the expert system’s effectiveness.
The manufacturing system that consists of the machines, tools, materials, product, quality control system, manufacturing methods and management, can be monitored and controlled by implementing the most cost effective maintenance policy. In Paper V, a new approach to an expert system concept is suggested. It benefits from the advantages of a common database, artificial neural network and fuzzy logic, and applies an expert system method in a more effective and reliable way. Using a common database ensures that the required data for particular purposes are available and continuously updated. The user interface is added to the expert system to provide a user-machine-expert system link, which in addition to the common database enables the expert system to be more flexible and continuously improved based on the new experience gained by the user or others. At the same time, artificial neural network, fuzzy logic and fuzzy neural network algorithms can be utilised in the inference engine part to enhance the certainty of data and remove any ambiguity or noise from the signal. Also, using fuzzy neural networks enables the controller to take proper action at the suitable time, due to their ability to reduce the time needed for learning. Finally, the beneficial integrated information available in the common database, which provides more accurate and comprehensive perception of the condition of the manufacturing system, production, product quality and costs results in more accurate diagnosis/prognosis and decisions. For more details see Paper V.