I. INTRODUCTION

II. BACKGROUND
A. EXPLAINABILITY AND INTERPRETABILITY IN ARTIFICIAL INTELLIGENCE
B. PREDICTIVE MAINTENANCE

III. SYSTEMATIC SEARCH
A. IDENTIFICATION
B. EXCLUSION CRITERIA AND SCREENING
C. INCLUSION

IV. SEARCH RESULTS

V. EXPLAINABLE AI IN PREDICTIVE MAINTENANCE
A. MODEL-AGNOSTIC
B. MODEL-SPECIFIC
C. COMBINATION OF METHODS

VI. INTERPRETABLE ML IN PREDICTIVE MAINTENANCE
A. ATTENTION
B. FUZZY-BASED
C. KNOWLEDGE-BASED
D. INTERPRETABLE FILTERS
E. DECISION TREES
F. FAULT TREES
G. PHYSICAL CONSTRAINTS
H. STATISTICAL METHODS
I. GRAPH ATTENTION NETWORKS (GATS)
J. GAUSSIAN MIXTURE MODEL
K. EXPLAINABLE BOOSTING MACHINE (EBM)
L. HIDDEN MARKOV MODEL
M. SPARSE NETWORKS
N. PROTOTYPE LEARNING
O. SIGNAL TEMPORAL LOGIC (STL)
P. DIGITAL TWIN
Q. SYMBOLIC LIFE MODEL
R. GENERALIZED ADDITIVE MODEL (GAM)
S. MAHALANOBIS-TAGUCHI SYSTEM (MTS)
T. K-NEAREST NEIGHBORS (KNN)
U. RULE-BASED INTERPRETATIONS

VII. CHALLENGES AND RESEARCH DIRECTIONS OF EXPLAINABLE PREDICTIVE MAINTENANCE
A. PURPOSE OF THE EXPLANATIONS
B. EVALUATION OF THE EXPLANATIONS
C. ADDITION OF HUMAN INVOLVEMENT
D. STUDY LIMITATIONS

VIII. CONCLUSION