Internet Supported Model Based Condition

A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528

Internet Supported Model Based Condition Monitoring +

A Lewlaski+, H. Alhajjar +, M Alhamar* and M Ebrahimi+ School of Engineering, Design and Technology, University of Bradford, UK * Department of Computer Science Loughborough University, UK Abstract

The importance of condition monitoring for preventive and predictive maintenance has increased through the use of system modelling. This modelling is carried out using manufacturer(s) information. Data collection using data acquisition cards provides raw data to support system monitoring, especially when used through internet and network facilities, which make it economically available for larger number of users. A model based condition monitoring system which utilises Internet is presented in this paper. The overall software/hardware for this system will be referred to as MBCM portable unit here. It contains a standalone model of system under consideration. The unit is capable of capturing signals directly from the system, saving them and producing these data in different formats for further analysis. In order to monitor the performance of the investigated system, the MBCM contains an embedded web-server to enable different signals to be monitored and captured locally, over a network, and via internet connection. 1. Introduction In order to achieve high quality performance and productivity, process monitoring has an important role in automated manufacturing plants. It is generally well known that cutting forces increase as tool-wear increases associated with the increase in friction between tool and work-piece [1]. Various direct and indirect monitoring devices have been used for tool condition monitoring purposes [2], and most approaches to tool condition monitoring are based upon three principle elements; sensors, feature extraction and decision making [3]. Dekker [4] defined Maintenance as “technical and associated administrative actions intended to retain a system in a state in which it can perform its required function.” A number of maintenance strategies are available and two are considered in this paper: Corrective maintenance and Preventive or Predictive maintenance [5]. The benefits gained from applying Preventive or Predictive maintenance include reducing the occurrence of failure, reducing planned maintenance, reducing spare component stocks and reducing overall costs [6]. Corrective maintenance can be characterised as involving “all unscheduled maintenance performed, as a result of failure, to restore the system to a specified condition.” [7]. Preventive or Predictive maintenance is defined by Taka et al [8] and Luxh et al [7] as “to measure and to analyse any parameter(s) that predicts remedial action”, is a relatively a new concept in maintenance planning and, depending on the data available, is used in predicting approximately when failure would occur if planned maintenance were not undertaken. In general, data such as vibration, acoustics, forces, temperature, chromatic, thermal, oil analyses, and visual observations or inspection, and so on are usually collected off line and analysed for trends. Preventive or Predictive maintenance is the main focus of this paper. Early condition monitoring systems consisted of single sensors sensing a single signal, which then evolved to complex, fuzzy relationships between different components in complex manufacturing systems [6], and their definition of condition monitoring as “monitoring the condition or the health of the system under consideration given that the system is acting correctly” can act as an advanced preventive or predictive maintenance system. Implementing condition monitoring for complex manufacturing systems within a manufacturing facility does not require the establishment of new records or diagnosing more difficult problems [9]. A fault diagnosing system should be able to detect intermittent as well as regular and gradual faults (e.g. wearout). In normal cases, the monitored signals are compared with historical data, but not all faults can be detected and analysed by condition monitoring. Also real fault development in terms of measured parameters requires gathering and analysing more information [9]. Fault diagnosis systems fall into two main categories– Expert based and Knowledge based. Expert based fault diagnosis systems use analytical mathematical models to simulate the system or process, and then test various hypotheses for fault detection and isolation. These systems are able to isolate faults at a very early stage. Knowledge based fault diagnosis systems use real-time statistics to evaluate faults as they happen. Iserman [10] [11] [12] introduces model based detection for looking at fault

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 symptom relationships, then describes some examples of common fault detection, diagnosis definitions and details of possible techniques that may be required for knowledge based procedures. There are many intelligent diagnosis systems that rely on vibration signals. These systems perform different tasks, depending upon the severity of the vibration signal. Fault diagnosis makes extensive use of vibration signals and there are many different techniques available in the literature. In 1996, Paya et al [13] investigated rotary drive trains driven by electric motors in a healthy condition and then intentionally introduced faults into the system with the aim of identifying multiple faults for bearing or gears. Mori et al [14] looked at how a neural network could be trained as part of an expert system for diagnosing undesirable vibrations. The expert system could then formulate a strategy based on fault definitions and rules to eliminate or reduce the vibrations using a knowledge base. To “judge the condition of internal components which are usually inaccessible without dismantling the machine…” he developed a technique using wavelet transform analysis of the signals and then training a neural network to identify specific single or multiple faults, which are indicated by changes in the vibration signal. Zhang et al [15] simplified a bearing to a single degree of freedom and looked at the acceleration response at the natural frequency. He then developed rules for damage mechanics relating to failure lifetime, running time and variation of stiffness of a bearing system. Nikolaou et al [16] analysed vibration signals from bearings with localized defects using wavelet transforms and found that, essentially, the signature of a damaged bearing contains a periodic ringing at a certain frequency. Li et al [17] used the acoustic emission (sound) from a defective outer race of bearing and applied digital signal processing techniques to extract dimensionless features that signify the defects. Zheng et al [18] used model based vibration monitoring to asses bearing failure severity. Non linear statistical techniques can be used to monitor non linear processes more efficiently. Zhang et al [15] tried to fit curves to measure data and applying non linear statistical techniques. Wang et al [19] developed a neural network that could handle non-linear systems and then combined the method with standard statistical techniques. Grosvenor et al [20] describe several methods of diagnosing faults. Two main methods are emphasised: a logical method which looks at the behaviour of certain components by giving a list of possible causes based on statistical data, and an algorithmic method that uses a tree of yes/no answers to questions that lead to all its common faults. A practical implementation of the system modelled the motor, rotary and linear motion components of the electromechanical drive system of a CNC machine tool. The data acquisition process captures output signals from sensors integrated within the machine tool for the purpose of condition monitoring, using a multifunction data acquisition card. Common signals acquired are vibration, speed, position, feed rates, force [22], motor current, and motor speed depending on the machine tool configuration. The model can compare motor speed, motor torque, rotary or linear positions and spindle speeds and can be adapted for different drive signals, or a different system model can be adopted as necessary. For the system under consideration the signals detected were the motor-torque, motor-speed, motor-current and feed rate of the machine table. 2. System structure In model-based condition monitoring systems, measured data from the system would be compared with simulated data from the model. This model would be constructed and parameterised using birth history data (operating parameters and normal signals from the machine in its initial ideal condition), which is given by the designer and manufacturer. The relationships between these different parameters would be affected under the condition of failures, so they are detected. Both decision-making and diagnosis of the monitored data are important for condition monitoring issues [9].

Figure 1 illustrates the system layout. In this manner different users can deal with the system as a black box, either locally or via web-browsers, as the only concern to a user are the input and output as indicated by the different arrows.

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528

Figure 1 MBCM System Layout

The MBCM portable unit is preloaded with a simulation software tool containing a model of the CNC electromechanical drive system. The model and simulation tool enables the user to perform simulation tests, experimental tests, comparison of results and fault diagnosis of CNC machine tool drive system. For comparison and fault diagnosis, the simulation software uses data files acquired by a data acquisition (DAQ) system that is also included in the system. All software is standalone and therefore requires no additional commercial software to function. By connecting the portable unit directly to the machine tool, the system can be run either locally by connecting a display monitor to the unit, remotely via a local TCP/IP network within the organisation, or remotely over the internet via web technology using a specified Graphical User Interface (GUI) if access to the unit is available. The use of the internet or network for data acquisition and condition monitoring facilitates testing different manufacturing systems and reducing the down time [21]. 3. Data Acquisition The sensors scanned can be any sensor that produces a ±5v output, either directly to an output pin, via a Digital to Analogue Converter (DAC), or after signal conditioning. Direct analogue output signals are those that are produced by devices and made available on an output link. Sensors that produce digital signals use a DAC to output an analogue signal at the output link. Signal conditioning is applied to devices that produce output other than an analogue signal. These signals are processed and/or amplified and converted to a  5v analogue signal. Testing of data acquisition was carried out using a CNC machine (Cincinnati Arrow 500) that produces a 24 bit digital drive signal. The DAC connected to the machine tool converts any contiguous 8 bit (or multiple of 8) sequence from the 24 bit drive signal selected by a machine tool user. The data acquisition card used for testing is a National Instruments 6024E multifunction data acquisition card. The card has eight analogue input channels at 12 bit resolution. A Visual Basic application accesses the NIDAQ driver via function calls. When accessing the portable unit the Visual Basic application enables a user to select the channels to scan, enter the parameters required by the data acquisition card and other supplementary information used by the data acquisition session, which includes identifiers for machine ID and process that can be used for further

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 development when required. The parameters and supplementary information are stored in a database. The signal data read from the scanned channels are stored in a binary file. When data acquisition has completed, the application reads the resultant binary file and converts (The conversion formula is contained in the documentation provided with the data acquisition card) the binary data into voltage values and stores them in a text file. Supplementary information and parameters related to the data acquisition session are stored at the beginning (header) of this file. Both parts of this text file are used as input for the model to run as shown in Figure 2.

Figure 2 Data flow from user input to text file

When accessing the system through the web, a user selects channels to scan and enters the parameters and supplementary information via a web page form. The values entered into the form are saved in a text file in a specific format and sequence. This text file is then used as input into a version of the Visual Basic application that does not include a GUI, and parses the text file and assigns the data values to variables. The variables used by the application are the same as if data was entered locally. Once again, data acquisition is performed; binary values are converted into voltage values, stored in a text file and passed to the model as shown in Figure 3. Time and date are added to filenames to ensure uniqueness.

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Figure 3 Schematic overview of the system

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 4. Database The database stores a permanent copy of the parameters required by the data acquisition card and any supplementary data for the session such as the channels scanned. This information serves to inform users of the conditions for the data acquisition session and also enables the data file to be recreated from the original binary file using appropriate software. The database format chosen is a relational database as the data is best suited to be saved in a tabular form [23]. A flat file database does not provide the accessibility required [24], and an object database [25] or XML database [26] are too complex for the requirements at this stage. The database selected is Microsoft Access in Access 2000 format using the Microsoft Jet 4.0 database engine. To access the database locally, a Visual Basic application using ADO controls is developed to access the database and display the results, either displaying all the records within a Data Grid control, or displaying individual records within a Visual Basic form. To access the database remotely over the Internet, a Data Access Page (DAP) is created to connect to the database. This dynamic web page displays 20 records per page. Each record has a hyperlink associated with it and selecting the link calls a second DAP that displays the individual record selected. 5. Model The model developed is a lumped model of a CNC machine tool drive system based on equations derived from Newton’s laws and was developed in block diagram form [27]. This model is written and compiled as a standalone executable application which runs independently of any installed commercial software. Function code was written that calls the model and passes the required input data to it, receiving the results and storing the results ready for post-processing. The code includes a Graphical User Interface (GUI) that behaves as a wrapper around the model and is the centre of all operations. As previously mentioned, two ways to run the code and perform a simulation are available; first is to run locally where the code is executed on the specific machine a user is working on, and secondly, to run remotely through a network or the internet where the user can access a remote machine that contains the code. Remote access is through web pages that are designed to be operationally similar to the local running. In both access methods (local or remote), the input data is passed to the model via an input data file and the model results are passed back to the function via output data file. Figure 4 shows how the data is passed to and from the model. The only difference being the way in which data is passed to or from the main function. When running locally, the user is presented with forms via the application, whereas remotely, the user is presented with forms via web pages, and then data is passed to the program via text files.

Input parameters (Machine Variables)

Store as an input data file

Pass input data file to Model EXE Plot / process results as user requires Model creates output data file

Figure 4 Data flow structure

There are three functions of the code wrapped around the model. These are to:Perform a Simulation Perform a comparison between Simulated and Experimental signals or perform a Fault Diagnosis. Look at Experimental signals

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 Before any of the above functions can be executed, some basic data must be available. For example, if a simulation is required, the machine parameters must be available. The required information for each function is shown in Figure 5, which also shows the sequence of operations.

DAQ Parameters

Test/ Experimental

m/c Parameters

Simulation

Comparison

Fault Diagnosis

Figure 5 Sequence of operations

The model is used for the simulation of a machine whose input parameters have been defined. The results from the model can be post-processed for either a comparison with test data or the required simulated output can be broken down into its frequency components by the FFT. (Fast Fourier Transform) Once broken down, any spurious frequencies can be readily identified. In order to perform a Simulation, the input parameters for the machine under investigation are required. The user can accept the default model parameters values provided or, in the case of modification, new parameters can be provided in one of two methods; either by entering new parameter values via an input form or by selecting a data file that contains previously entered parameter values. After the model has been simulated, the user can then view a selected simulated output signal(s). The simulation is restricted in that only one type of machine (one type of model) can be simulated at any one time, depending on the lumped model used/loaded [20]. A comparison will provide the user with a plot of the simulated signal and the test signal on a single display. As in simulation mode, a user either enters parameters via a form or selects a file to provide the input parameters for the machine to be simulated. For the test signal, a user can either; enter test parameters via a form and then initiate a data acquisition session to provide a new test file, or select a previous test file that contains the relevant test signal, so that a comparison can be made. After the model has been simulated, the user selects the required simulated output signals and the test signals file so that a valid comparison can be made. Using a motor current signal as an example for Fault diagnosis, the model will provide the user with a graphical plot of the FFT of the motor current signal from a test file. The user is required to provide the input parameters of the machine for simulation, and also a valid test file that contains the motor current signal When the model has been simulated, the user will be prompted for motor current test signal (real signals from the machine DAQ) so that it can be decomposed into its frequency components and compared with the FFT of the simulated signal (virtual signal(s) from the model). The user can view the graphical plot of a signal from any test file. The user will be prompted for the file containing the test signals so that they can be plotted. 6. Web Implementation Web technologies were used to develop an interface for the MBCM-G system. Microsoft Front Page was used to create static HyperText Markup Language (HTML) web pages and PHP scripting language was used to create Dynamic Web Pages. The Abyss Web Server was chosen in order to provide an easy configuration. The PHP interpreter is installed as a CGI processor to collect web page form data and generate dynamic HTML pages. The relation between HTML and PHP is that PHP can generate HTML, and HTML can pass information to PHP. To access the system via the web, the user connects to the web server using the IP address of the MBCM-G portable unit, supplied by the network administrator of the network that the unit is connected to. Any web browser can be used to access the model functionality. To access the unit’s database via the web, either Microsoft Office 10 or Microsoft Office Web Components 10 (owc10) are required to be installed on the client computer in order to access the Data Access Pages that interface the database.

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 The initial page contains links to Simulation, Experimental, Comparison, and Fault Diagnosis functions. Simulation (Figure 6) uses previous data files or initiates a new data acquisition session. Experimental (Figure 7) runs the model according to selected parameters. For the Comparison and Fault Diagnosis functions, data acquisition data are compared to model outputs and fault diagnosis applied to the results. Previous data acquisition sessions and model parameter files are saved permanently within the system directory. To create new data acquisition or model parameters files a user can enter parameters, or select default values,. The pathname and filename of the files selected by the user, either new or previous, are saved in a specific text file, in a specific folder, and this text file is accessed by the model to determine the input data files to use.

Figure 6 Simulation web access flow chart

Figure 7 Experimental web access flow chart

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Figure 8 Comparison and fault diagnosis web access flow chart

The system comprises of different functions as described earlier, and each function is processed within it’s own directory space. The advantage of this modular construction allows individual functions to be amended independently. System functionality is divided into two main parts. The first part is related to Data Acquisition while the second part is related to System Modelling. Parameters forms for both data acquisition (Figure 9) and model parts (Figure 10) are shown. When a user starts a new session any files stored in the working directory are deleted and the user is requested to enter new parameters or to select any previous files. The files created during previous sessions are stored in a temporary folder and time-stamped for unique identification, with the 10 previous sessions being made available. All sessions are saved in a designated folder and also made available. The older sessions are archived at periodic intervals, for example, on an annual basis (excluding normal backup procedures), and saved to CD media. Files on the unit itself can be archived to CD, or accessed from a CD by attaching an external CD/RW drive to the unit, or transferred over the internet to designated storage via FTP technology.

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Figure 9: MBCM-G Data Acquisition variables Web access form

Figure 10: MBCM-G model parameters form through web access

When the program is accessed remotely, a user interacts through web pages. Each web page is designed to have the same functionality and similar looks and to the original forms when running locally. The user can enter for example, the machine input data on the relevant webpage. This data is then written to a text file. The program will then open up the text file and read all data from the text file and then create the input data file required by the model. The model will then run as before and create an input data file ready for post-processing. Once each step of the process has been completed, the user is presented with the next webpage to continue the condition monitoring session, for example, to view output plots (Figures 12 and 13). When the simulation is run, one of the code’s post-processing features is the automatic generation of a JPG image file, so that a user can view the results over the Internet.

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 The general data flow, whether running locally or remotely is shown below in Figure 11.

Direct Input for Local Access

Input parameters data file

Pass input data file to Model EXE

Text File created by Webpage for Remote Access

Model creates output data file Figure windows of plots Plot / process results as user requires JPG files created for webpage

User chooses JPG files to view Figure 11 General Data Flow

7. Results When the system is accessed locally the results of the data acquisition session are plotted by the model using it’s own integral plot commands and when accessing the system via web browsers the same results are plotted and saved as jpg image(s) and presented to the user within a dynamic web page created by the PHP scripting language installed with the webserver. Figure 12 shows an example plot for motor current with table feed at 30 m/min created with the integral plotter within the model when used locally, while Figure 13 shows the same plot when accessed via the Internet.

Figure 12: MBCM-G local access plot for motor current at 30 m/min feed rate

Figure 13: MBCM-G remote access plot for motor current at 30 m/min feed rate

Conclusion The model of the CNC machine has been successfully deployed to a standalone executable file removing the requirement for any commercial software to be on the system. The model can be run locally on the machine that the user is working on, or run remotely by dialling into the webpage system. All interaction between the user and the program is done directly through the GUI system and all plots are shown as ‘live’ figures. The user can interact indirectly with the program in the remote running, in this mode, the user interacts with the machine via

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A Lewlaski et. al. / International Journal of Engineering Science and Technology Vol. 2(4), 2010, 517-528 WebPages and provides information to the WebPages. The WebPages then create text files that are passed to the program. The information is read from the text files and the relevant part of the program is executed. Trial of the MBCM-G on a CNC machine has demonstrated a correlation between the machine axis drive motor current signature from its initial conditions in the model with current conditions existing on the machine. This has validated the principle of a portable model based condition-monitoring unit. Access to the model via the internet has also been demonstrated. By further extension of the model, other machine configurations such as a servo spindles with servo feed axes could be evaluated. Also, introduction of other types of sensors such as accelerometers for monitoring vibrations of the machine mechanisms would extend the application of condition monitoring. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]

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