Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

A 31-year-old male noticed blurred vision in his right eye for five days with no obvious predisposing causes, accompanied by mild dizziness. No obvious nodular lesions were found in the body. The patient’s binocular visual acuity was 20/20. Fundus photography showed the optic nerve swelling and radial superficial retinal hemorrhage of both eyes. Blood panel, urine routine, liver, and kidney function were all normal. Total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein were all in the normal limits. Head MRI showed a mass in the right temporal lobe, clear boundary, and multiple separations, which thinned and disappeared closer to the skull. The right temporal lobe and lateral ventricle were all compressed, with the midline structure shifted to the left. The patient was then transferred to Neurosurgery. During the operation, we observed the tumor had invaded the skull. The actual size of the tumor was 5.6 cm × 7.5 cm × 10.1 cm. Histology revealed foam cell accumulation in the mucous connective tissue of the right temporal lobe. The immunohistochemistry showed: CD34 (+), CD99 (+), EMA (−), GFAP (−), IDH-1 (−), Ki-67 (+) index about 10%, Oliga-2 (−), PR (−), S-100 (−), Vim (+), β-Catenin (+), CD1a (−), CD68 (+). Three months after the removal of the tumor, the visual acuity of both eyes was 20/20; The visual fields were normal, the optic disc edema and retinal hemorrhages had disappeared. MRI indicated the midline structure was back to normal.