Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: There is described herein methods and systems relating to the use of the interaction of different materials to perform early detection of component failure. In particular, when the debris in a fluid sample comes from more than one source, a “level of interaction” is determined in order to monitor the degradation of the parts or components associated with the debris, as a function of the composition of the particles found in the fluid sample.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for diagnosing a condition of an engine based on lubrication fluid analysis are disclosed. One embodiment of the methods comprises: receiving input data representative of a respective geometric parameter and a respective chemical composition for a plurality of particles filtered from a sample of fluid obtained from the engine; generating data representative of a mass of material of a chemical composition category in one or more of the particles; comparing the mass of material of the chemical composition category with reference data; and generating output data representative of a diagnosis of the condition of the engine.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for diagnosing a condition of an engine based on lubrication fluid analysis are disclosed. One embodiment of the methods comprises: receiving input data representative of a respective geometric parameter and a respective chemical composition for a plurality of particles filtered from a sample of fluid obtained from the engine; generating data representative of a mass of material of a chemical composition category in one or more of the particles; comparing the mass of material of the chemical composition category with reference data; and generating output data representative of a diagnosis of the condition of the engine.

Abstract: Methods and systems for diagnosing a condition of an engine based on lubrication fluid analysis are disclosed. One embodiment of the methods comprises: receiving input data representative of a respective geometric parameter and a respective chemical composition for a plurality of particles filtered from a sample of fluid obtained from the engine; generating data representative of a mass of material of a chemical composition category in one or more of the particles; comparing the mass of material of the chemical composition category with reference data; and generating output data representative of a diagnosis of the condition of the engine.

Abstract: There is described herein methods and systems relating to the use of the interaction of different materials to perform early detection of component failure. In particular, when the debris in a fluid sample comes from more than one source, a “level of interaction” is determined in order to monitor the degradation of the parts or components associated with the debris, as a function of the composition of the particles found in the fluid sample.

Abstract: There is described herein methods and systems relating to the use of the interaction of different materials to perform early detection of component failure. In particular, when the debris in a fluid sample comes from more than one source, a “level of interaction” is determined in order to monitor the degradation of the parts or components associated with the debris, as a function of the composition of the particles found in the fluid sample.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.

Abstract: Methods and systems for diagnosing a condition of an engine based on lubrication fluid analysis are disclosed. One embodiment of the methods comprises: receiving input data representative of a respective geometric parameter and a respective chemical composition for a plurality of particles filtered from a sample of fluid obtained from the engine; generating data representative of a mass of material of a chemical composition category in one or more of the particles; comparing the mass of material of the chemical composition category with reference data; and generating output data representative of a diagnosis of the condition of the engine.

Abstract: There is described herein methods and systems relating to the use of the interaction of different materials to perform early detection of component failure. In particular, when the debris in a fluid sample comes from more than one source, a “level of interaction” is determined in order to monitor the degradation of the parts or components associated with the debris, as a function of the composition of the particles found in the fluid sample.

Abstract: Methods and systems for failure prediction using analysis of oil or other lubricant. Raw data about feature(s) of each of a plurality of particles filtered from a fluid sample are used to categorize each particle into one of a plurality of categories, each category being defined by one or more of: chemical composition, size and morphology. Particle physical characteristics in each category are quantified to obtain a set of categorized data. The categorized data are compared with historical data. Results of the comparing are evaluated to generate a prediction of any failure or mechanism of failure.