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Tool Condition Monitoring (TCM) methods have shown significant potential to automatically detect worn tools without intervention in the machining process, thus decreasing machine downtime and improving reliability and part quality. Previous research on TCM systems have used a wide variety of time-domain and frequency-domain features extracted from cutting force related parameters as well as mechanical and acoustical vibrations to infer the wear state of tools. This project concerns the process of drilling thousands of tight-tolerance holes on tubesheets and baffles of heat exchangers using large diameter indexable insert drills on a horizontal boring machine. To address the issues involved in the process, the aim of this research is to develop a non-intrusive, indirect, online TCM system on the horizontal boring machine to monitor the drill wear and hole quality while drilling. The specific objectives are to establish an indirect TCM system for the drilling process, to develop models to predict tool wear and the machining accuracy of the drilled holes, and to develop an optimum tool replacement strategy. The TCM system developed used two cutting-force related signals on the horizontal boring machine, namely the spindle motor current and the axial feed motor current. Features extracted from these data streams, as well as the machining parameters, the cutting speed and the feed rate, and the number of holes drilled with the current inserts, are the inputs to a series of models to predict the tool wear state and the hole diameter. The first model is an autoregressive model that allows the prediction of the extracted features for the next hole before it is drilled. As each hole is drilled, this model is updated with the most recent data to improve the accuracy of the prediction. The predicted values for the features are then used as inputs to the second and third models which are surface response models, one to estimate the tool wear state and one to estimate the hole diameter. A tool replacement strategy based on applying limits to the predicted hole diameter was also developed. Adjusting these limits allows the strategy to be tuned for either hole accuracy or tool life depending on the requirements of a specific application. Tuning the replacement strategy for tool life resulted in a significant 44% increase in tool life and a non-trivial reduction in machine down time due to fewer tool changes while holding a hole diameter tolerance of ±0.1mm. The TCM system ensured that not a single over tolerance hole would have been drilled which is critically important since over tolerance holes can result in a scrapped workpiece. The proposed 3-model TCM system shows promise in being able to significantly reduce the risk of drilling out of tolerance holes while at the same time increasing tool life and correspondingly decreasing tool change time. The models are able to accurately predict the insert flank wear and as well as the actual hole diameter within acceptable error. The TCM system could be implemented in an industrial settingwith minimal revision and since it is an indirect system there would be no intrusion into the manufacturing operation. One limitation of the TCM system as proposed is that it is only capable of detecting gradual tool wear and not catastrophic tool failure, a limitation that was known from the outset but was not investigated as it was beyond the scope of this project. The proposed TCM system would allow the integration of additional functionality to instantaneously detect catastrophic tool failure. Finally, for use in a production environment, the developed models need to be implemented on a standalone device that requires essentially no operator input to monitor continuous drilling operations for tubesheet and baffle applications. This implementation could include automatic detection of the machining parameters using frequency analysis of the motor signals.

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Condition based monitoring is widely used for the determination of the health of machines. The Nottingham Trent University Computing Department has developed a new system, the Incremental Motion Encoder (!ME), which is based on the time interpolation of the digital signals produced by an optical encoder. Experiments have shown that the !ME can be used as a condition based maintenance sensor as it is possible to detect rolling element defects, an unbalanced shaft and oil contamination of a bearing. The system uses a geometrically configured optical device to scan a precision encoder disc and Digital Signal Processing technology is used to interpret the signals. Previous work has demonstrated the qualitative usefulness of the 1ME. However, further work was needed to assess the accuracy of the measurements, to analyse the principles of the 1ME, to validate the performance of the existing device and to develop methods for error definition and error compensation. Testing and experimentation on the existing experimental system have been carried out by the Candidate and an understanding gained of the device. The sources of error of the 1ME have been identified, which had not been quantified previously. Measuring and compensating for the three main sources of error, read head position, eccentricity of the encoder disc and encoder abnormalities are the three major tasks of the project. Modifications to the experimental rig have been developed in order to allow these tasks to be addressed. The Candidate has developed three different types of techniques to measure the position error of the read heads. A pattern recognition method was developed and is successful for 1ME systems that use an encoder disc with significant grating line errors. A second method using Fast Fourier Transform (FFT) has been developed to exploit the fact that the difference in the phase angles, obtained using a FFT, gives the angle between the read head positions. The new experimental system is now able to obtain the angular position of the read heads by using the index grating line. The third method relies on the presence of the index grating line on the encoder disc which may not be present in all systems. Eccentricity of disc centre relative to the centre of rotation affects the correct calculation of the angular position of the encoder disc. Algorithms have been developed by the Candidate in order to compensate for this type of error. Experimental results have shown that angular position error can be corrected successfully. The Candidate has developed methods for detection of small abnormalities of the encoder disc by using a multiple averaging technique. Computational algorithms have been developed to correct the encoder disc abnormalities by using individual information from each read head, promising results have been obtained from the experimental 1ME. An 1ME device can be tailored to fulfil the desired requirements of resolution, bandwidth and accuracy. A self calibration instrument can be developed by using the previously mentioned techniques in order to self calibrate and increase the accuracy and reliability of an IME's results.

Condition monitoring business has been of interest to Sulzer since the 1990s when the Sulzer Diagnostic System (SUDIS) was developed. However, since the invention of SUDIS, Sulzer has had limited commercial success with condition monitoring products and services. Several recent investigations at Sulzer have explored possibilities for new machine monitoring business, with the most recent being the condition monitoring equipment survey of Nyitray. This report leads on from the work of Nyitray to evaluate the attractiveness of current machine monitoring markets and the strength of current and concept control and monitoring business models. Sales and customer support services (CSS) staff in all business segments and areas were surveyed for information regarding customer demand for machine monitoring solutions. The results of the survey lacked a unified view on customer needs, indicating that customer needs vary significantly with industry and region. Results also indicated that Sulzer sales and CSS staff currently have very little contact with customers regarding machine monitoring issues, which was expected since Sulzer currently has very limited machine monitoring offers. Overall customer interest in machine monitoring for cost saving purposes is high. Moreover, some customers expect equipment manufactures like Sulzer to support their equipment with machine monitoring offers. Business model environmental factors for each Sulzer Pumps focus market were identified allowing the most attractive markets for machine monitoring business to be selected. A survey of Sulzer Pumps business segment heads also contributed to the market selection process. This evaluation concluded that the water and power generation industries had favourable markets for machine monitoring business, mainly because Sulzer has a good competitive position in these markets. Unfortunately pumps in the electricity generation industry are relatively reliable compared to other machinery such as electrical generators. Hence, opportunities identified in the electricity generation industry seemed to be more appropriate for Sulzer Turbo Services than Sulzer Pumps. However, cross-divisional collaboration of Sulzer Pumps and Sulzer Turbo services would allow Sulzer to offer solutions for entire drivetrains. Other opportunities suitable for Sulzer Pumps were identified in the district heating and water industries, with energy monitoring being a common theme. An evaluation of the oil pipeline industry yielded that there is significant market demand for machine and pipeline monitoring. However, Sulzer currently does not have the experience or resources to provide the demanded monitoring services independently. Hence searching for key partners or acquisition targets was acknowledged as an essential activity for Sulzer Pumps to enter this market. Another means of market entry would be to develop novel technology or integrate emerging technologies (e.g. online viscosity sensors) into new pipeline monitoring solutions, i.e. to create a novel value proposition. Subsequent feedback from Sulzer alliance managers concluded that oil pipeline customer acquisition may be difficult since many pipeline companies already have monitoring solutions which they are satisfied with. Hence the oil pipeline market is not recommended for new machine monitoring business ventures. ABS pump control and monitoring solutions are currently the only machine monitoring solutions offered by Sulzer Pumps. In this study the business model behind these solutions was analysed to evaluate its strength and identify areas for improvement. Although the ABS control and monitoring business model is profitable, sales figures are below their potential. Recommendations to improve the business models effectiveness mainly focused on improving channels through which Sulzer connects with its customers. These included improving Sulzer digital marketing material, improving product selection tools, increasing complementary advertising and quotation contents to connect Sulzer control and monitoring products with pump equipment products, and most importantly increasing the amount of sales and CSS staff training.

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