This paper tests the effect of multi-frequency vibrations on the power output of piezoelectric
energy harvesters. These tests were conducted to show how the output power scales when an energy harvester is excited not only at its eigenfrequency, but also at an off-resonance frequency and how well theoretical models predict power output under these conditions. A linear cantilever and bistable buckled beam are tested experimentally on an electrodynamic shaker and simulated using existing models to compare the accuracy of these models under such excitations. The excitation signal has one fixed frequency component, which matches the eigenfrequency of the energy harvester; and a second vibration component, which has a frequency and amplitude that are varied. For a linear cantilever, it was shown that an existing closed-parameter model can predict power output for the mechanical and electrical parameters, where the power is a summation of the power generated by each separate frequency. However, even a simple cantilever shows nonlinearity for high excitations; this is something one needs to account for if you use the model to calculate expected power output.
For a nonlinear harvester it was shown that the power output scales linear with the amplitude of vibration when subjected to a single sine. For multi-sine vibrations, the power output is only slightly increased by adding the second frequency, indicating that the beneficial bandwidth of a nonlinear energy harvester is much smaller when absorbing energy from a second frequency.

Modern internet–based technologies require devices that can generate, acquire, and
compute data. Typically, these devices rely on conventional batteries, which limit device
size, lifespan, and introduce maintenance costs due to replacements.
A promising alternative is using piezoelectric energy harvesters, which convert mechanical
stress into voltage via the piezoelectric effect. These harvesters need to be activated
by external vibrations, but most current systems operate in a frequency range of 50 to
300 Hz. However, ambient vibrations often occur at lower frequencies (5 to 40 Hz) and
are not constant,making it challenging to harvest energy efficiently when the frequency
mismatch occurs.
The objective of this research is to develop a design that can harvest energy from a broad
range of low and non–constant frequencies and develop a testing methodology for its
dynamic characterization that represents a real–world scenario, by using square wave
and noise excitation.
The proposed design is based on the frequency up–conversion principle. The design
features a driving oscillator, a bi–stable oscillator that resonates at low frequencies and
collects energy during its snap–through motion. The generating oscillator, on the other
hand, resonates at higher frequencies and contains the piezoelectric material. The energy
harvesting is achieved by the transfer of energy from the driving to the generating
oscillator.
The dynamic characterization of the design resulted in several findings. The proposed
design can up–convert frequency by a factor of 208 and harvest energy at low input vibration
(0.1–20 Hz) and noise excitation (0–300 Hz).
Overall this research contributes to the field of energy harvesting by introducing a new
design that can harvest energy fromambient vibrations and a new testing procedure that
replicates a real–world scenario.

The thesis titled "Towards Smart Maintenance: The Implementation of Predictive Maintenance in the Railway Industry" focuses on integrating predictive maintenance (PdM) technologies into railway operations, emphasizing freight rail. PdM employs data analytics, machine learning, and IoT-enabled sensors to shift from traditional maintenance approaches to a proactive, data-driven methodology. This transformation promises reduced downtime, optimized resource use, and enhanced system reliability.

The research identifies technical, financial, and organizational challenges as key barriers to PdM adoption, including data standardization, algorithm maturity, high initial investment, and resistance to change. Drawing lessons from industries like aviation and infrastructure, the study highlights phased implementation, regulatory frameworks, and stakeholder collaboration as critical enablers.

Employing tools like Interpretive Structural Modeling and Fuzzy MICMAC, the thesis maps interdependencies among barriers and demonstrates PdM's potential for reducing maintenance costs by up to 47.8% over two decades. It concludes with recommendations for systematic adoption, addressing both technical and managerial factors, to ensure safer and more efficient railway maintenance practices.