This page contains a Flash digital edition of a book.
possible if the right maintenance tech- nologies and people are chosen and supported with investment in training. In terms of maintenance phi-


losophies, running to fail is the most expensive, followed by scheduled, periodic maintenance. Not all mainte- nance can be performed on a periodic basis, so a condition-based component can be added to a dominant periodic maintenance program. A next step in maintenance is to use a predictive maintenance plan with a condition- based component. Te longer you can extend the time


to failure on the equipment, the better the ROI becomes. Te bathtub curve in Fig. 1 shows that the sooner a failure mode is detected and corrected, the cost of the repair will be lower and the need for replacement will be delayed. Aarrowcast Inc. (Shawano, Wiscon- sin) uses various forms of work requests or work instructions that form the inputs and outputs of its computerized main- tenance management system. Operator- based maintenance is ordered, per- formed and tracked just like scheduled maintenance. Predictive maintenance procedures are scheduled, performed, and tracked as well. When any of the processes are reviewed, the CMMS is updated to the desired change. Retaining the processes in this way


protects Aarrowcast and its customers against the loss of intellectual assets. If a technician leaves, a training docu- ment is available to sustain the process. Aarrowcast’s predictive maintenance system involves equipment operators working closely with maintenance technicians to make sure the processes are working as defined. Maintenance and production supervisors are the first level of quality assurance, while the predictive maintenance group is the second level. Te operators are given periodic training on predictive main- tenance because they are the ones who do most of the routine operational inspections and lubrications. Every time a new asset comes


online, Aarrowcast performs baseline monitoring and equipment commis- sioning. Te testing processes and equipment qualifies Aarrowcast to make sure the equipment performs as specified in the purchase order. Te


Table 1. Estimated Investment in Data Collectors


Investment


IR Thermography Ultrasound DCA Vibration DCA


Motor DC/Analyzer Power Logger


Misc. Other Electrical Misc. E/M Test Equip.


Cost 4,000


11,000 22,000 25,000 6,000 5,000 5,000


to a proportional range of frequen- cies humans can hear. Certain noises, based on their origin, can be tuned in and given a decibel value. A bearing’s noise level, for example, is trended using a 25 kHz frequency. As this level is tracked, the technician can moni- tor whether it needs lubrication or is entering a fault condition. Aarrowcast uses ultrasonic analysis


metalcaster can test the equipment’s performance before the supplier leaves the premises, preventing costly returns and contract disputes.


Predictive Maintenance Technologies At Aarrowcast a number of predic-


tive maintenance technologies are used, including vibration analysis, thermal imaging, ultrasonic analysis, oil analysis, and power condition and monitoring. Aarrowcast’s vibration analysis database includes more than 1,600 data points with 87-91% updated weekly or bi-weekly. Te remaining are updated as opportunity or needs arise. Te detection of problems has improved and increased as training and experi- ence has grown. Ultrasonic shock pulse monitoring recently has been added to its suite of automatic vibra- tion tests if the device being tested is rotating at less than 600 RPM. Infrared cameras perform thermal imaging of electrical components and check the bearing, gearbox, refrac- tory and bin levels temperatures for hotspots. Te detection of an unde- sired temperature (either too hot or too cold) has long been used to find problem areas. Tis technology assists others, such as vibration and ultra- sound, to provide additional infor- mation about what stage of failure the device being tested may be in. Temperature loggers attached to the process enables Aarrowcast to calcu- late trend values for time/load/func- tion correlations. Friction and turbulence produce


ultrasonic noise the human ear cannot hear. Ultrasonic instruments are tuned to frequencies as high as 100 kHz. Te unit processes the signal down


in conjunction with infrared imaging on electrical apparatus because ultra- sound can “hear” a fault condition that infrared may not “see” and vice versa. Ultrasonic analysis can be used


to test valve functions and pump operations. Te detection of airborne ultrasonic turbulence generated by a compressed air leak can be used to reduce energy losses from a com- pressed air supply. Oil analysis, which Aarrowcast


outsources to the oil supplier, is conducted on samples taken during scheduled maintenance or as a special sample taken to back up vibration or ultrasonic technologies. Results are reviewed and work orders generated to respond to needs as determined. Routine online and offline motor


tests on critical equipment through- out the plant can be used to evaluate power supply problems and monitor motor conditions. Offline motor test procedures


include gathering inductive, capacitive and resistive values in the de-energized motor circuit. Te motor’s insulation properties are evaluated and trended along with the levels of imbalnance that exist among the components. Other testing and data collecting


procedures include stroke monitor- ing of vibratory conveyors, dew point monitoring, laser alignments, precision lubrication, fan wheel balancing, root cause failure analysis and continuous process improvement. Stroke monitoring can be used to


monitor the wear in drive compo- nents and how it effects the stroke of the conveyor. Dew point monitoring of the air discharging from com- pressed air driers helps in scheduling desiccant material changes when it’s necessary, instead of on a time-based schedule. Te testing procedures also involve making sure the unit


October 2016 MODERN CASTING | 27


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60