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Although deep convolutional neural networks (CNNs) achieve high accuracy on public benchmarks, their large parameter counts and computational costs hinder deployment on resource-constrained edge devices. This paper proposes a frequency-aware lightweight CNN (FAL-CNN) that reconciles competitive accuracy with extreme efficiency. A fixed, non-trainable pipeline (fast Fourier transform, log compression, linear resampling, and min\u2013max normalization) converts each 1024-sample vibration segment into a compact 128-dimensional spectrum, reducing the input eightfold. A frequency-aware module applies three parallel one-dimensional convolutions with kernels of 3, 7, and 15, matched to single-peak, harmonic-pair, and harmonic-family fault signatures, followed by a depthwise-separable backbone that sharply reduces parameters and floating-point operations (FLOPs). All Case Western Reserve University (CWRU) experiments use a leakage-controlled protocol with time-blocked splits, a sample-isolation gap, and five random seeds. Across four operating loads, FAL-CNN reaches a 99.78% mean accuracy with 65,122 parameters and 3.88 M multiply-accumulate operations (MACs) (7.76 M FLOPs), inferring in 0.17 ms on a single CPU thread (0.27 ms end-to-end) within a 288.8 KB Open Neural Network Exchange (ONNX) footprint. Because full-data accuracy saturates for all compact models, we evaluate where architecture matters: under operating-load shift, FAL-CNN outperforms a parameter-matched plain CNN by 13.27 percentage points (88.42% vs. 75.15%); frequency-masking and attribution analyses confirm that the multi-scale filters concentrate on fault frequency bands (a 21.91-percentage-point targeted-masking gap, roughly twice that of structural controls); under 10:1 training class imbalance, it maintains a macro-F1 above 99.2%; and on the Paderborn real damage benchmark with bearing-level nested validation, it attains (59.86 \u00b1 18.86)% accuracy on unseen bearings, the best mean among the compared pipelines, although the difference from the parameter-matched baseline is not statistically significant. These results position FAL-CNN as a compact, edge-oriented classifier whose measured feasibility is currently limited to the development CPU; physical edge-device profiling remains required. Its demonstrated value lies in reliability under the evaluated distribution shifts rather than in saturated benchmark accuracy.<\/jats:p>","DOI":"10.3390\/s26185861","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:50:27Z","timestamp":1789566627000},"page":"5861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Frequency-Aware Lightweight Convolutional Neural Network for Edge-Enabled Intelligent Bearing Fault Diagnosis and Health Management"],"prefix":"10.3390","volume":"26","author":[{"given":"Hao","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shitong","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbiao","family":"Gui","sequence":"additional","affiliation":[{"name":"Taizhou Changxing Rail Transit Operation Management Co., Ltd., Taizhou 318000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijie","family":"Zou","sequence":"additional","affiliation":[{"name":"Taizhou Changxing Rail Transit Operation Management Co., Ltd., Taizhou 318000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Taizhou Changxing Rail Transit Operation Management Co., Ltd., Taizhou 318000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling element bearing diagnostics\u2014A tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mech. 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