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Current Projects

Some of the projects we are currently working on are:

·         Wind turbine condition monitoring 

·         Helicopter gearbox early failure signature detection

·         Gearbox fault diagnosis

·         Real time engine oil debris monitoring and analysis

·         Condition-based machinery health management using streamlined sensing systems

·         Integrated machine and process fault detection and diagnosis

·         Chatter detection and suppression in machining processes

·         Detection of tool wear and breakage using acoustic methods

·         Intelligent control of CNC machining processes

·         Integrated manufacturing/production planning and control

Demonstrations

·         Demo1: In-line oil debris detection

           This demonstration shows the metal particle detection using on-line oil-debris monitoring (ODM) sensor under 2-directional vibrations. The lubricant oil passes the sensing region of the sensor through the tube. To simulate the severe vibration near the engine in practice, the shakers were used to apply external vibrations in the horizontal and the vertical directions. The particle detection algorithm was written using the NI LabVIEW.

·         CNC on-line machining control

       Introduction

          CNC milling machines with open-loop control are designed to rigidly execute NC codes regardless of the on-line cutting geometry variation, tool conditions, and the dynamics of the cutting process, thus leading to under-utilization of machine capacity, unsafe machining processes, and excessive human intervention. This situation can be improved by performing either off-line planning or on-line control.  
    The main idea of off-line planning is to apply different feed rates for different depth of cut, width of cut, or volume of material to be removed. This is mostly done off-line before the actual machining process, ignoring the on-line cutting and tool conditions. In addition, this method adjusts only feed rate, overlooking the mismatch of feed rate and spindle speed, and thus causing low productivity, chatters, and pre-mature tool failure.  
    On the other hand, the on-line control approach adjusts machining parameters in response to the tool conditions, thus providing near optimal material removal rate. Current CNC on-line machining control methods use traditional control algorithms and dynamometers, and thus are sensitive to machine, cutter, and workpiece material changes. In addition, they all require lengthy modeling processes, which do not suit today’s machining environment, i.e. small batch sizes, frequent tool, workpiece and machine changes. Another disadvantage is that dynamometer are very expensive, in the range of $30,000~$60,000. The workpieces are usually mounted on the top of a dynamometer. A dynamometer could be ruined due to the direct exposure to chips, cutting fluid, and possible overload. In addition, the use of dynamometers involves tedious setups and may cause instability.
    To avoid those problems, a novel on-line machining control system has been developed at the University of Ottawa which has the following features. 
   a)      Capable of adjusting multiple machining parameters.
   b)      Insensitive to machine-tool-workpiece changes.
   c)      Non-intrusive, i.e., it does not interrupt normal cutting processes and it does not restrict workpiece sizes.
   d)     Inexpensive and durable. 
    The new control system was designed using fuzzy logic control based on power input. The fuzzy control is robust because it tolerates imprecise or incomplete information and knowledge and does not require exact information about the dynamics of a system and hence avoids lengthy modeling process. Due to its robustness, the same system can be implemented on different machines.  
    Power sensors are in-expensive, non-intrusive and durable. The power sensor is separated from the cutting process, and therefore it does not restrict workpiece size and does not require additional setups. Since the power sensor is not exposed to the harsh cutting conditions, the fluids, chips, potential overload and collision can not cause any damage to the sensor.
　

·         Fault detection

       Play the Video

·         Oil debris signature extraction

       Play the Video

Major Funding

The activities of this research group are funded in part by:

·         NSERC Discovery Grant

·         NSERC Research Tools and Instruments Grant

·         Ontario Centers of Excellence Directed Collaborative Research Program

Collaborations

·         GasTOPS, Ltd. (Canada)

·         National Research Council (Canada)

·         Okuma (USA)