• PRCI NB-16
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PRCI NB-16

  • Wear in Pipeline Engines and Compressors - Methods of Measurement
  • Report / Survey by Pipeline Research Council International, 05/01/1959
  • Publisher: PRCI

$6.00$12.00


L00200e

Institute of Gas Technology

Need: A two-year study of the wear history of 84 pipeline compressor engines in relation to the concentrations of metals in the lubricating oils from these engines has demonstrated the feasibility of using lubricating oil analyses as a means of detecting increased rate of wear of engine components prior to their failure in service. Inexpensive emission spectrographic analyses were used to determine the metal content of the lubricating oil samples in the parts per million ranges.


Result: The non-detergent lubricating oils from most of the engines, which operated normally during the study contained low concentrations of, wear metals. It was, therefore, possible to indicate tentative upper limits for metal concentrations in non-detergent oils from normally operating engines, However, these limits are applicable only to engines in which oil is well filtered or sufficient new makeup oil is added to keep the wear metal concentrations down. Other exceptions were found where metal-containing lubricants and sealing compounds entered the oil in trace amounts. Several instances of abnormal ring and bearing wear, which occurred during the test program, were indicated by an increase in iron, lead or tin concentrations in the engine lubricating oil up to several months in advance of the necessary part replacement. Although the highest sensitivity to wear rate changes is obtained with clean, non-detergent oils, the method is still useful with detergent oils which tend to retain larger amounts of wear metals in suspension.

Benefit: The information obtained in this study will permit individual pipeline companies to evaluate the merits of lubricating oil analysis as a preventive maintenance tool for their own operations, and will furnish a basic understanding of the methods used to properly interpret the analytical data.

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