Logging-while-drilling (LWD) formation evaluation requires reliable sensing in a high-temperature, high-pressure, shock, and vibration environment. This paper presents a pulsed-neutron LWD architecture that uses one neutron generator and multiple dual-function scintillation detectors to measure neutrons and neutron-induced gamma rays. Pulse-shape discrimination separates the two radiation responses, and near, middle, and far detector count-rate ratios provide measurements with different depths of investigation. A trained neural network fuses three neutron ratios, three capture-gamma ratios, and mineralogy to estimate formation porosity; environmental variables such as borehole size, tool standoff, salinity, temperature, and pressure may be included when available. To align the method with equipment health monitoring, the paper further proposes a vibration-aware measurement-health diagnosis layer. Tri-axial vibration, angular rate, temperature, toolface, source status, pulse-shape separation quality, count-rate balance, and cross-detector consistency are synchronized with the nuclear measurements. Condition indicators are used to distinguish formation response from vibration disturbance, detector gain drift, optical-coupling change, source-output variation, electronics intermittency, and telemetry loss. A multi-task model then produces a porosity estimate, a measurement-health state, and confidence. Redundant detectors at different spacings and toolface angles provide additional evidence for fault isolation and degraded-channel operation. The original tool architecture and porosity workflow are retained as the engineering basis; because synchronized vibration and maintenance labels are not reported in the source data, the health-diagnosis extension is presented as a testable framework rather than a validated classifier.

Vibration-aware sensing,intelligent diagnosis,equipment health monitoring,logging while drilling,pulsed neutron,neural network,formation porosity