###### Abstract  
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine's current operational state by analyzing time-frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine's future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated. DC motor; diagnosing; AdaBoost; weak classifiers; fault severity conditions; remaining useful life +
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initiate the combustion process in internal combustion engines. The onset of gear wear, for instance, can manifest as diminished torque output and increased mechanical noise during operation, adversely affecting the engine's ignition process and overall performance [4]. Similarly, misaligned or damaged gear teeth can exacerbate these issues, leading to sub-optimal engine starting capabilities and potential safety hazards. Given the critical role of DC motor gear systems in ensuring the reliable operation of automotive systems, the development of comprehensive diagnostic and maintenance strategies is imperative [4]. By leveraging advanced predictive maintenance techniques, such as condition monitoring and fault prognostics, automotive engineers can proactively identify and address potential gear faults before they escalate into critical failures. Additionally, ongoing research efforts aimed at optimizing gear design, material selection, and lubrication practices hold promise for further enhancing the resilience and durability of DC motor gear systems in automotive applications. Moving forward, diagnostics of electrical machinery encompass three main techniques: model-based, signal-based, and data-based [5]. It is possible to use signal-based techniques in the time, frequency, and time-frequency domains [6]. The classification of faults also makes use of artificial intelligence and pattern recognition techniques [7]. A variety of time-frequency transformations were applied, such as the Choi-Williams, Wigner-Ville, short-time Fourier, and undecimated wavelet transforms [8]. To identify and categorize nonstationary defects in permanent magnet AC machines at an early stage, these transformations were coupled with classification techniques, including the multiple discriminant-based classifier, the closest neighborhood classifier, and the linear discriminant classifier [9]. In contrast to prognosis, diagnosis has been the main focus of study. More research has been conducted in the recent literature on fault prognosis, which includes estimating remaining useful life (RUL) and projecting future states [10]. One frequently used prognostic method revolves around particle filters, especially when it comes to forecasting the RUL of rotary systems experiencing problems like cracks [11]. Moreover, similarity-based prognostic techniques play a role in estimating RUL. These methods involve examining the life patterns of machines derived from run-to-failure data found in training datasets [12]. Predicting the RUL of a test sample involves matching patterns against these established ones. Another notable approach to prognosis employs neural networks, particularly in forecasting the RUL of batteries [13]. Both similarity-based prognostication and neural networks are categorized as data-based methods.